https://wiki.cosmos.esa.int/planckpla2015/api.php?action=feedcontributions&user=Mlopezca&feedformat=atomPlanck PLA 2015 Wiki - User contributions [en-gb]2022-11-28T06:03:18ZUser contributionsMediaWiki 1.31.6https://wiki.cosmos.esa.int/planckpla2015/index.php?title=CMB_and_astrophysical_component_maps&diff=11654CMB and astrophysical component maps2020-04-21T13:06:56Z<p>Mlopezca: /* Thermal dust maps */</p>
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<div>{{DISPLAYTITLE:2015 CMB and astrophysical component maps}}<br />
<br />
== Overview ==<br />
This section describes the maps of astrophysical components produced from the Planck data. These products are derived from some or all of the nine frequency channel maps described above using different techniques and, in some cases, using other constraints from external data sets. Here we give a brief description of each product and how it is obtained, followed by a description of the FITS file containing the data and associated information.<br />
All the details can be found in {{PlanckPapers|planck2014-a11}} and {{PlanckPapers|planck2014-a12}}.<br />
<br />
==CMB maps==<br />
CMB maps have been produced using four different methods: COMMANDER, NILC, SEVEM, and SMICA, as described in the [[Astrophysical_component_separation#CMB_and_foreground_separation | CMB and foreground separation]] section and also in Appendices A-D of {{PlanckPapers|planck2014-a11}} and references therein.<br />
<br />
'''As discussed extensively in {{PlanckPapers|planck2014-a01}}, {{PlanckPapers|planck2014-a07}}, {{PlanckPapers|planck2014-a09}}, and {{PlanckPapers|planck2014-a11}}, the residual systematics in the Planck 2015 polarization maps have been dramatically reduced compared to 2013, by as much as two orders of magnitude on large angular scales. Nevertheless, on angular scales greater than 10 degrees, correponding to l < 20, systematics are still non-negligible compared to the expected cosmological signal.'''<br />
<br />
'''It was not possible, for this data release, to fully characterize the large-scale residuals from the data or from simulations. Therefore all results published by the Planck Collaboration in 2015 which are based on CMB polarization have used maps which have been high-pass filtered to remove the large angular scales. We warn all users of the CMB polarization maps that they cannot yet be used for cosmological studies at large angular scales.'''<br />
<br />
'''For convenience, we provide as default polarized CMB maps from which all angular scales at l < 30 have been filtered out. '''<br />
<br />
For each method we provide the following:<br />
* Full-mission CMB intensity map, confidence mask and beam transfer function.<br />
* Full-mission CMB polarisation map, <br />
* A confidence mask.<br />
* A beam transfer function.<br />
In addition, and for characterisation purposes, we include six other sets of maps from three data splits: first/second half-ring, odd/even years and first/second half-mission. For the year-1,2 and half-mission-1,2 data splits we provide half-sum and half-difference maps which are produced by running the corresponding sums and differences inputs through the pipelines. The half-difference maps can be used to provide an approximate noise estimates for the full mission, but they should be used with caution. Each split has caveats in this regard: there are noise correlations between the half-ring maps, and missing pixels in the other splits. The Intensity maps are provided at Nside = 2048, at 5 arcmin resolution, while the Polarisation ones are provided at Nside = 1024, at 10 arcmin resolution. All maps are in units of K<sub>cmb</sub>.<br />
<br />
In addition, for each method we provide three sets of files, each categorized by the "R2.0X" label as follows:<br />
<br />
; ''R2.02''<br />
<pre style="white-space: pre-wrap; <br />
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This set of intensity and polarisation maps are provided at a resolution of Nside=1024. The Stokes Q and U maps are high-pass filtered to contain only modes above l > 30, as explained above and as used for analysis by the Planck Collaboration; THESE ARE THE POLARISATION MAPS WHICH SHOULD BE USED FOR COSMOLOGICAL ANALYSIS. Each type of map is packaged into a separate fits file (as for "R2.01"), resulting in file sizes which are easier to download (as opposed to the "R2.00" files), and more convenient to use with commonly used analysis software.<br />
</pre><br />
<br />
; ''R2.01''<br />
<pre style="white-space: pre-wrap; <br />
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This is the most complete set of 2015 CMB maps, containing Intensity products at a resolution of Nside=2048, and both Intensity and Polarisation at resolution of Nside=1024. For polarisation (Q and U), they contain all angular resolution modes. WE CAUTION USERS ONCE AGAIN THAT THE STOKES Q AND U MAPS ARE NOT CONSIDERED USEABLE FOR COSMOLOGICAL ANALYSIS AT l < 30. The structure of these files is the same as for "R2.02".<br />
</pre><br />
<br />
; ''R2.00''<br />
<pre style="white-space: pre-wrap; <br />
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This set of files is equivalent to the "R2.01" set, but are packaged into only two large files. Warning: downloading these files could be very lengthy...<br />
</pre><br />
<br />
For a complete description of the above data structures, see [[#File names and structure | below]]; the content of the first extensions is illustrated and commented in the table below.<br />
<br />
<br />
The gallery below shows the Intensity, noise from half-mission, half-difference, and confidence mask for the four pipelines, in the order COMMANDER, NILC, SEVEM and SMICA, from top to bottom. The Intensity maps' scale is [–500.+500] μK, and the noise spans [–25,+25] μK. We do not show the Q and U maps since they have no significant visible structure to contemplate.<br />
<br />
<center><br />
<gallery style="padding:0 0 0 0;" perrow=3 widths=300px heights=180> <br />
File:CMB_commander_tsig.png | '''commander temperature'''<br />
File:CMB_commander_tnoi.png | '''commander noise'''<br />
File:CMB_commander_tmask.png | '''commander mask'''<br />
File:CMB_nilc_tsig.png | '''nilc temperature'''<br />
File:CMB_nilc_tnoi.png | '''nilc noise'''<br />
File:CMB_nilc_tmask.png | '''nilc mask'''<br />
File:CMB_sevem_tsig.png | '''sevem temperature'''<br />
File:CMB_sevem_tnoi.png | '''sevem noise'''<br />
File:CMB_sevem_tmask.png | '''sevem mask'''<br />
File:CMB_smica_tsig.png | '''smica temperature'''<br />
File:CMB_smica_tnoi.png | '''smica noise'''<br />
File:CMB_smica_tmask.png | '''smica mask'''</gallery><br />
</center><br />
<br />
===Product description ===<br />
<br />
====COMMANDER====<br />
<br />
;Principle<br />
<br />
: COMMANDER is a Planck software code implementing pixel based Bayesian parametric component separation. Each astrophysical signal component is modelled in terms of a small number of free parameters per pixel, typically in terms of an amplitude at a given reference frequency and a small set of spectral parameters, and these are fitted to the data with an MCMC Gibbs sampling algorithm. Instrumental parameters, including calibration, bandpass corrections, monopole and dipoles, are fitted jointly with the astrophysical components. A new feature in the Planck 2015 analysis is that the astrophysical model is derived from a combination of Planck, WMAP and a 408 MHz (Haslam et al. 1982) survey, providing sufficient frequency support to resolve the low-frequency components into synchrotron, free-free and spinning dust. For full details, see {{PlanckPapers|planck2014-a12}}.<br />
<br />
; Resolution (effective beam)<br />
<br />
: The Commander sky maps have different angular resolutions depending on data products:<br />
* The components of the full astrophysical sky model derived from the complete data combination (Planck, WMAP, 408 MHz) have a 1 degree FWHM resolution, and are pixelized at N<sub>side</sub>=256. The corresponding CMB map defines the input map for the low-l Planck 2015 temperature likelihood. <br />
* The Commander CMB temperature map derived from Planck-only observations has an angular resolution of ~5 arcmin and is pixelized at N<sub>side</sub>=2048. This map is produced by harmonic space hybridiziation, in which independent solutions derived at 40 arcmin (using 30-857 GHz data), 7.5 arcmin (using 143-857 GHz data), and 5 arcmin (using 217-857 GHz data) are coadded into a single map.<br />
* The Commander CMB polarization map has an angular resolution of 10 arcmin and is pixelized at N<sub>side</sub>=1024. As for the temperature case, this map is produced by harmonic space hybridiziation, in which independent solutions derived at 40 arcmin (using 30-353 GHz data) and 10 arcmin (using 100-353 GHz data) are coadded into a single map.<br />
<br />
; Confidence mask<br />
<br />
: The Commander confidence masks are produced by thresholding the chi-square map characterizing the global fits, combined with direct CO amplitude thresholding to eliminate known leakage effects. In addition, we exclude the 9-year WMAP point source mask in the temperature mask. For full details, see Sections 5 and 6 in {{PlanckPapers|planck2014-a12}}. A total of 81% of the sky is admitted for high-resolution temperature analysis, and 83% for polarization analysis. For low-resolution temperature analysis, for which the additional WMAP and 408 MHz observations improve foreground constraints, a total of 93% of the sky is admitted. <br />
<br />
====NILC====<br />
<br />
;Principle<br />
<br />
: The Needlet-ILC (hereafter NILC) CMB map is constructed both in total intensity as well as polarization: Q and U Stokes parameters. For total intensity, all Planck frequency channels are included. For polarization, all polarization sensitive frequency channels are included, from 30 to 353 GHz. The solution, for T, Q and U is obtained by applying the Internal Linear Combination (ILC) technique in needlet space, that is, with combination weights which are allowed to vary over the sky and over the whole multipole range. <br />
<br />
; Resolution (effective beam)<br />
<br />
: The spectral analysis, and estimation of the NILC coefficients, is performed up to a maximum <math>\ell=4000</math>. The effective beam is equivalent to a Gaussian circular beam with FWHM=5 arcminutes. <br />
<br />
; Confidence mask<br />
<br />
: The same procedure is followed by SMICA and NILC for producing confidence masks, though with different parametrizations. A low resolution smoothed version of the NILC map, noise subtracted, is thresholded to 73.5 squared micro-K for T, and 6.75 squared micro-K for Q and U.<br />
<br />
<br />
====SEVEM====<br />
; Principle<br />
<br />
: SEVEM produces clean CMB maps at several frequencies by using a procedure based on template fitting in real space. The templates are typically constructed from the lowest and highest Planck frequencies and then subtracted from the CMB-dominated channels, with coefficients that are chosen to minimize the variance of the clean map outside a considered mask. In the cleaning process, no assumptions about the foregrounds or noise levels are needed, rendering the technique very robust. Two single frequency clean maps are then combined to obtain the final CMB map.<br />
<br />
;Resolution<br />
<br />
: For intensity the clean CMB map is constructed up to a maximum <math>\ell=4000</math> at Nside=2048 and at the standard resolution of 5 arcminutes (Gaussian beam).<br />
: For polarization the clean CMB map is produced at Nside=1024 with a resolution of 10 arcminutes (Gaussian beam) and a maximum <math>\ell=3071</math>.<br />
<br />
; Confidence masks<br />
<br />
: The confidence masks cover the most contaminated regions of the sky, leaving approximately 85 per cent of useful sky for intensity, and 80 per cent for polarization.<br />
<br />
=====Foregrounds-subtracted maps=====<br />
<br />
In addition to the regular CMB maps, SEVEM provides maps cleaned of the foregrounds for selected frequency channels (categorized as fgsub-sevem in the archive). In particular, for intensity there are clean CMB maps available at 100, 143 and 217 GHz, provided at the original resolution of the uncleaned channel and at Nside=2048. For polarization, there are Q/U clean CMB maps for the 70, 100 and 143 GHz (at Nside=1024). The 70 GHz clean map is provided at its original resolution, whereas the 100 and 143 GHz maps have a resolution given by a Gaussian beam with fwhm=10 arcminutes.<br />
<br />
====SMICA====<br />
; Principle<br />
: SMICA produces CMB maps by linearly combining all Planck input channels with multipole-dependent weights. It includes multipoles up to <math>\ell = 4000</math>. Temperature and polarization maps are produced independently.<br />
; Resolution (effective beam)<br />
: The SMICA intensity map has an effective beam window function of 5 arc-minutes which is truncated at <math>\ell=4000</math> and is '''not''' deconvolved from the pixel window function. Thus the delivered beam window function is the product of a Gaussian beam at 5 arcminutes and the pixel window function for <math>N_{side}</math>=2048.<br />
: The SMICA Q and U maps are obtained similarly but are produced at <math>N_{side}</math>=1024 with an effective beam of 10 arc-minutes (to be multiplied by the pixel window function, as for the intensity map).<br />
; Confidence mask<br />
: A confidence mask is provided which excludes some parts of the Galactic plane, some very bright areas and masked point sources. This mask provides a qualitative (and subjective) indication of the cleanliness of a pixel. See section below detailing the production process.<br />
<br />
<br />
==== Common Masks====<br />
<br />
A number of common masks have been defined for analysis of the CMB temperature and polarization maps. They are based on the confidence masks provided by the component separation methods. One mask for temperature and one mask for polarization have been chosen as the preferred masks based on subsequent analyses.<br />
<br />
The common masks for the CMB temperature maps are:<br />
<br />
* UT78: union of the Commander, SEVEM, and SMICA temperature confidence masks (the NILC mask was not included since it masks much less of the sky). It has f<sub>sky</sub> = 77.6%. This is the preferred mask for temperature.<br />
<br />
* UTA76: in addition to the UT78 mask, it masks pixels where standard deviation between the four CMB maps is greater than 10 &mu;K. It has f<sub>sky</sub> = 76.1%.<br />
<br />
The common masks for the CMB polarization maps are:<br />
<br />
* UP78: the union of the Commander, SEVEM and SMICA polarization confidence masks (the NILC mask was not included since it masks much less of the sky). It has f<sub>sky</sub> = 77.6%.<br />
<br />
* UPA77: In addition to the UP78 mask, it masks pixels where the standard deviation between the four CMB maps, averaged in Q and U, is greater than 4 &mu;K. It has f<sub>sky</sub> = 76.7%.<br />
<br />
* UPB77: in addition to the UP78 mask, it masks polarized point sources detected in the frequency channel maps. It has f<sub>sky</sub> = 77.4%. This is the preferred mask for polarization.<br />
<br />
====CMB-subtracted frequency maps ("Foreground maps")====<br />
<br />
These are the full-sky, full-mission frequency maps in intensity from which the CMB has been subtracted. The maps contain foregrounds and noise. They are provided for each frequency channel and for each component separation method. They are grouped into 8 files, two for each method of which there is one for each instrument. The maps are are at N<sub>side</sub> = 1024 for the three LFI channels and at N<sub>side</sub> = 2048 for the six HFI channels. The filenames are:<br />
<br />
* ''LFI_Foregrounds-{method}_1024_Rn.nn.fits'' (145 MB each)<br />
* ''HFI_Foregrounds-{method}_2048_Rn.nn.fits'' (1.2 GB each)<br />
<br />
To remove the CMB, the respective CMB map was first deconvolved with the 5 arcmin beam, then convolved with the beam of the frequency channel, and finally subtracted from the frequency map. This was done using the <math>B_\rm{l}</math> in harmonic space, assuming a symmetric beam.<br />
<br />
The CMB-subtracted maps have complicated noise properties. The CMB maps contain a noise contribution from each of the frequency maps, depending on the weights with which they were combined. Therefore subtracting the CMB map from a frequency channel contributes additional noise from the other frequency channels.<br />
<br />
The frequency maps from which the CMB have been subtracted are:<br />
<br />
* ''LFI_SkyMap_0nn_1024_R2.01_full.fits''<br />
* ''HFI_SkyMap_nnn_2048_R2.0n_full.fits''<br />
<br />
Note that the temperature column in the HFI R2.00, R2.01 and R2.02 is the same, since the changes in these maps involved the polarization columns only. Also note that the zodiacal light correction described [https://wiki.cosmos.esa.int/planckpla2015/index.php/Map-making#Zodiacal_light_correction here] was applied to the HFI temperature maps before the CMB subtraction.<br />
<br />
====Quadrupole Residual Maps====<br />
<br />
The second-order (kinematic) quadrupole is a frequency-dependent effect. During the production of the frequency maps the frequency-independent part was subtracted, which leaves a frequency-dependent residual quadrupole. The residuals in the component-separated CMB temperature maps have been estimated by simulating the effect in the frequency maps and propagating it through the component separation pipelines. The residuals have an amplitude of around 2 &mu;K peak-to-peak. The maps of the estimated residuals can be used to remove the effect by subtracting them from the CMB maps.<br />
<br />
===Production process===<br />
<br />
====COMMANDER====<br />
<br />
; Pre-processing<br />
<br />
: All sky maps are first convolved to a common resolution that is larger than the largest beam of any frequency channel. For the combined Planck, WMAP and 408 MHz temperature analysis, the common resolution is 1 degree FWHM; for the Planck-only, all-frequency analysis it is 40 arcmin FWHM; and for the intermediate-resolution analysis it is 7.5 arcmin; while for the full-resolution analysis, we assume all frequencies between 217 and 857 GHz have a common resolution, and no additional convolution is performed. For polarization, only two smoothing scales are employed, 40 and 10 arcmin, respectively. The instrumental noise rms maps are convolved correspondingly, properly accouting for their matrix-like nature. <br />
<br />
; Priors<br />
<br />
: The following priors are enforced in the Commander analysis:<br />
* All foreground amplitudes are enforced to be positive definite in the low-resolution analysis, while no amplitude priors are enforced in the high-resolution analyses<br />
* Monopoles and dipoles are fixed to nominal values for a small set of reference frequencies<br />
* Gaussian priors are enforced on spectral parameters, with values informed by the values derived in the high signal-to-noise areas of the sky<br />
* The Jeffreys ignorance prior is enforced on spectral parameters in addition to the informative Gaussian priors<br />
<br />
; Fitting procedure<br />
<br />
: Given data and priors, Commander either maximizes, or samples from, the Bayesian posterior, P(theta|data). Because this is a highly non-Gaussian and correlated distribution, involving millions of parameters, these operations are performed by means of the Gibbs sampling algorithm, in which joint samples from the full distributions are generated by iteratively sampling from the corresponding conditional posterior distributions, P(theta_i| data, theta_{j/=i}). For the low-resolution analysis, all parameters are optimized jointly, while in the high-resolution analyses, which employs fewer frequency channels, low signal-to-noise parameters are fixed to those derived at low resolution. Examples of such parameters include monopoles and dipoles, calibration and bandpass parameters, thermal dust temperature etc.<br />
<br />
====NILC====<br />
<br />
; Pre-processing<br />
<br />
: All sky frequency maps are deconvolved using the DPC beam transfer function provided, and re-convolved with a 5 arcminutes FWHM circular Gaussian beam. In polarization, prior to the smoothing process, all sky E and B maps are derived from Q and U using standard HEALPix tools from each individual frequency channels <br />
<br />
; Linear combination<br />
<br />
: Pre-processed input frequency maps are decomposed in needlet coefficients, specified in the Appendix B of the Planck A11 paper, with shape given by Table B.1. Minimum variance coefficients are then obtained, using all channels for T, from 30 to 353 for E and B. <br />
<br />
; Post-processing<br />
<br />
: E and B maps are re-combined into Q and U products using standard HEALPix tools. <br />
<br />
====SEVEM====<br />
<br />
The templates used in the SEVEM pipeline are typically constructed by subtracting two close Planck frequency channel maps, after first smoothing them to a common resolution to ensure that the CMB signal is properly removed. A linear combination of the templates <math>t_j</math> is then subtracted from (hitherto unused) map d to produce a clean CMB map at that frequency. This is done in real space at each position on the sky: <math> T_c(\mathbf{x}, ν) = d(\mathbf{x}, ν) − \sum_{j=1}^{n_t} α_j t(\mathbf{x}) </math><br />
where <math>n_t</math> is the number of templates. The <math>α_j</math> coefficients are obtained by minimising the variance of the clean map <math>T_c</math> outside a given mask. Note that the same expression applies for I, Q and U. Although we exclude very contaminated regions during the minimization, the subtraction is performed for all pixels and, therefore, the cleaned maps cover the full-sky (although we expect that foreground residuals are present in the excluded areas).<br />
<br />
There are several possible configurations of SEVEM with regard to the number of frequency maps which are cleaned or the number of templates that are used in the fitting. Note that the production of clean maps at different frequencies is of great interest in order to test the robustness of the results, and these intermediate products (clean maps at individual frequencies for intensity and polarization) are also provided in the archive. Therefore, to define the best strategy, one needs to find a compromise between the number of maps that can be cleaned independently and the number of templates that can be constructed.<br />
<br />
;Intensity<br />
<br />
For the CMB intensity map, we have cleaned the 100 GHz, 143 GHz and 217 GHz maps using a total of four templates. Three of them are constructed as the difference of two consecutive Planck channels smoothed to a common resolution (30-44, 44-70 and 545-353) while the 857 GHz channel is chosen as the fourth template. First of all, the six frequency channels which are going to be part of the templates are inpainted at the point source positions detected using the Mexican Hat Wavelet algorithm. The size of the holes to be inpainted is determined taking into account the beam size of the channel as well as the flux of each source. The inpainting algorithm is based on simple diffuse inpainting, which fills one pixel with the mean value of the neighbouring pixels in an iterative way. To avoid inconsistencies when subtracting two channels, each frequency map is inpainted on the sources detected in that map and on the second map (if any) used to construct the template. Then the maps are smoothed to a common resolution (the first channel in the subtraction is smoothed with the beam of the second map and viceversa). For the 857 GHz template, we simply filter the inpainted map with the 545 GHz beam.<br />
<br />
The coefficients are obtained by minimising the variance outside the analysis mask, that covers the 1 per cent brightest emission of the sky as well as point sources detected at all frequency channels. Once the maps are cleaned, each of them is inpainted on the point sources positions detected at that (raw) channel. Then, the MHW algorithm is run again, now on the clean maps. A relatively small number of new sources are found and are also inpainted at each channel. The resolution of the clean map is the same as that of the original data. Our final CMB map is then constructed by combining the 143 and 217 GHz maps by weighting the maps in harmonic space taking into account the noise level, the resolution and a rough estimation of the foreground residuals of each map (obtained from realistic simulations). This final map has a resolution corresponding to a Gaussian beam of fwhm=5 arcminutes.<br />
<br />
In addition, the clean CMB maps produced at 100, 143 and 217 GHz frequencies are also provided. The resolution of these maps is the same as that of the uncleaned frequency channels and have been constructed at Nside=2048. They have been inpainted at the position of the detected point sources. Note that these three clean maps should be close to independent, although some level of correlation will be present since the same templates have been used to clean the maps.<br />
<br />
The confidence mask is produced by studying the differences between several SEVEM CMB reconstructions, which correspond to maps cleaned at different frequencies or using different analysis masks. The obtained mask leaves a useful sky fraction of approximately 85 per cent.<br />
<br />
;Polarization<br />
<br />
To clean the polarization maps, a procedure similar to the one used for intensity data is applied to the Q and U maps independently. However, given that a narrower frequency coverage is available for polarization, the selected templates and maps to be cleaned are different. In particular, we clean the 70, 100 and 143 GHz using three templates for each channel. The first step of the pipeline is to inpaint the positions of the point sources using the MHW, in those channels which are going to be used in the construction of templates, following the same procedure as for the intensity case. The inpainting is performed in the frequency maps at their native resolution. These inpainted maps are then used to construct a total of four templates. To trace the synchrotron emission, we construct a template as the subtraction of the 30 GHz minus the 44 GHz <br />
map, after being convolved with the beam of each other. For the dust emission, the following templates are considered: 353-217 GHz (smoothed at 10' resolution), 217-143 GHz (used <br />
to clean 70 and 100 GHz) and 217-100 GHz (to clean 143 GHz). These two last templates are constructed at 1 degree resolution since an additional smoothing becomes necessary in<br />
order to increase the signal-to-noise ratio of the template. Conversely to the <br />
intensity case and due to the lower availability of frequency channels, it becomes necessary to use the maps to be cleaned as part of one of the templates. In this way, the 100 GHz <br />
map is used to clean the 143 GHz frequency channel and viceversa, making the clean maps less independent between them than in the intensity case.<br />
<br />
These templates are then used to clean the non-inpainted 70 (at its native resolution), 100 (at 10' resolution) and 143 GHz maps (also at 10'). The corresponding linear coefficients are estimated independently for Q and U by minimising the variance of the clean maps outside a mask, that covers point sources and the 3 per cent brightest Galactic emission. Once the maps have been cleaned, inpainting of the point sources detected at the corresponding raw maps is carried out. The size of the holes to be inpainted takes<br />
into account the additional smoothing of the 100 and 143 GHz maps. The 100 and 143 GHz clean maps are then combined in harmonic space, using E and B decomposition, to produce the final CMB maps for the Q and U components at a resolution of 10' (Gaussian beam) for a HEALPix parameter Nside=1024. Each map is weighted taking into account its <br />
corresponding noise level at each multipole. Finally, before applying the post-processing HPF to the clean polarization data, the region with the brightest Galactic residuals is inpainted (5 per cent of the sky) to avoid the introduction of ringing around the Galactic centre in the filtering process.<br />
<br />
The clean CMB maps at individual frequency channels produced as intermediate steps of SEVEM are also provided for Q and U, constructed at Nside=1024. The clean 70 GHz map is provided at its native resolution, while the clean maps at 100 and 143 GHz frequencies have a resolution of 10 arcminutes (Gaussian beam). The three maps have been inpainted in the positions of the detected point sources. Note that, due to the availability of a smaller number of templates for polarization than for intensity, these maps are less independent than for the temperature case, since, for instance, the 100 GHz map is used to clean the 143 GHz one and viceversa.<br />
<br />
The confidence mask includes all the pixels above a given threshold in a smoothed version of the clean CMB map, the regions more contaminated by the CO emission and those pixels more affected by the high-pass filtering, leaving a useful sky fraction of approximately 80 per cent.<br />
<br />
<br />
<br />
====SMICA====<br />
<br />
A) Production of the intensity map.<br />
<br />
; 1) Pre-processing<br />
: Before computing spherical harmonic coefficients, all input maps undergo a pre-processing step to deal with regions of very strong emission (such as the Galactic center) and point sources. The point sources with SNR > 5 in the PCCS catalogue are fitted in each input map. If the fit is successful, the fitted point source is removed from the map; otherwise it is masked and the hole is filled in by a simple diffusive process to ensure a smooth transition and mitigate spectral leakage. The diffusive inpainting process is also applied to some regions of very strong emissions. This is done at all frequencies but 545 and 857 GHz, here all point sources with SNR > 7.5 are masked and filled-in similarly.<br />
; 2) Linear combination<br />
: The nine pre-processed Planck frequency channels from 30 to 857 GHz are harmonically transformed up to <math>\ell = 4000</math> and co-added with multipole-dependent weights as shown in the figure.<br />
; 3) Post-processing<br />
: A confidence mask is determined (see the Planck paper) and all regions which have been masked in the pre-processing step are added to it.<br />
<br />
<!--[[File:Smica_filter_dx11.png|thumb|center|600px|'''Weights given by SMICA to the input intensity maps (after they are re-beamed to 5 arc-minutes and expressed in K<math>_\rm{RJ}</math>), as a function of multipole.''']]--><br />
<br />
B) Production of the Q and U polarisation maps.<br />
<br />
The SMICA pipeline for polarization uses all the 7 polarized Planck channels. The production of the Q and U maps is similar to the production of the intensity map. However, there is no input point source pre-processing of the input maps. The regions of very strong emission are masked out using an apodized mask before computing the E and B modes of the input maps and combining them to produce the E and B modes of the CMB map. Those modes are then used to synthesize the U and Q CMB maps. The E and B parts of the input frequency maps being processed jointly, there are, at each multipole, 2*7=14 coefficients (weights) defined to produce the E modes of the CMB map and as many to produce the B part. The weights are displayed in the figure below. The Q and U maps were originally produced at Nside=2048 with a 5-arc-minute resolution, but were downgraded to Nside=1024 with a 10 arc-minute resolution for this release.<br />
<br />
<!--[[File:Smica_filterEB_dx11.png|thumb|center|600px|'''Weights given by SMICA to the input E and B modes (after they are re-beamed to 5 arcmin and expressed in K<math>_\rm{RJ}</math>), in order to produce the E and B modes of the CMB map. A given frequency channel is encoded in a given color. Solid lines are for E modes and dashed lines are for B modes. The thick lines are for the EE or BB weights; the thin lines are for the EB or BE weights. See the paper for more details.''']]--><br />
<br />
====Masks====<br />
Summary table with the different masks that have been used by the component separation methods to pre-process and to process the frequency maps and the CMB maps.<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|-<br />
|- bgcolor="ffdead" <br />
! Commander 2015 (PR2) || Used for diffuse inpainting of input frequency maps || Used for constrained Gaussian realization inpaiting of CMB map || Description<br />
|-<br />
|TMASK || NO || NO || TMASK (the equivalent to PR1 VALMASK) is the confidence mask in temperature that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits|link=COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits}}.<br />
|-<br />
|PMASK || NO || NO || PMASK is the confidence mask in polarization that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-commander_1024_R2.02_full.fits|link=COM_CMB_IQU-commander_1024_R2.02_full.fits}}.<br />
|-<br />
|INP_MASK_T || NO || YES || Three masks have been used for inpaiting of CMB maps for specific <math>\ell</math> ranges: three different angular resolution maps (40 arcmin, 7.5 arcmin and full resolution), are produced using different data combinations and foreground models. Each of these are inpainted with their own masks with a constrained Gaussian realization before coadding the three maps in harmonic space.<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_n0256_lmax200_0256_R2.03.fits|link=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_n0256_lmax200_0256_R2.03.fits}}<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_lmax1000_2048_R2.03.fits|link=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_lmax1000_2048_R2.03.fits}}<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_fullres_2048_R2.03.fits|link=COM_Mask_PointSrcGalplane_commander_dx11d2_temp_fullres_2048_R2.03.fits}}<br />
|-<br />
|INP_MASK_P || NO || YES || Mask used for inpainting of the CMB map in polarization.<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_commander_dx11d2_pol_fullres_1024_R2.02.fits|link=COM_Mask_PointSrcGalplane_commander_dx11d2_pol_fullres_1024_R2.02.fits}}<br />
|- bgcolor="800000"<br />
|<br />
! ||<br />
|<br />
|- bgcolor="ffdead" <br />
! SEVEM 2015 (PR2) || Used for Diffuse Inpainting of foregorund subtracted CMB maps (fgsub-sevem) || Used for constrained Gaussian realization inpaiting of CMB map || Description<br />
|-<br />
|TMASK || NO || NO || TMASK (the equivalent to PR1 VALMASK) is the confidence mask in temperature that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-sevem-field-Int_2048_R2.01_full.fits|link=COM_CMB_IQU-sevem-field-Int_2048_R2.01_full.fits}}.<br />
|-<br />
|PMASK || NO || NO || PMASK is the confidence mask in polarization that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-sevem_1024_R2.02_full.fits|link=COM_CMB_IQU-sevem_1024_R2.02_full.fits}}.<br />
|-<br />
|INP_MASK_T || YES || NO || Point source mask for temperature. This mask is the combination of the 143 and 217 T point source masks used for the inpainting of the foreground subtracted CMB maps at those two frequencies. These two maps have been combined to produce the final CMB map. <br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_extended143x217_2048_R2.00.fits|link=COM_Mask_PointSrc_sevem_extended143x217_2048_R2.00.fits}}<br />
|-<br />
|INP_MASK_P || YES || NO || Point source mask for polarization. This mask is the combination of the 100 and 143 point source masks used for the inpainting of the foreground subtracted CMB maps at those two frequencies. These two maps have been combined to produce the final CMB map.<br />
* {{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_extended100x143_fw10_1024_R2.00.fits|link=COM_Mask_PointSrc_sevem_extended100x143_fw10_1024_R2.00.fits}}<br />
|-<br />
|INP_MASK_T for the cleaned 100, 143 and 217 GHz CMB || YES || NO || Three temperature point source masks used for the inpainting of the foreground subtracted CMB maps at the considered frequencies: <br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_ps100_extended_2048_R2.00.fits|link=COM_Mask_PointSrc_sevem_ps100_extended_2048_R2.00.fits}} (clean 100 GHz)<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_ps143_extended_2048_R2.00.fits|link=COM_Mask_PointSrc_sevem_ps143_extended_2048_R2.00.fits}} (clean 143 GHz)<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_ps217_extended_2048_R2.00.fits|link=COM_Mask_PointSrc_sevem_ps217_extended_2048_R2.00.fits}} (clean 217 GHz)<br />
|-<br />
|INP_MASK_P for the cleaned 70, 100 and 143 GHz CMB|| YES || NO || Three polarization point source masks used for the inpainting of the foreground subtracted CMB maps at the considered frequencies:<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_dx11d_70_pol_varhole_full_99p90_1024_R2.00.fits|link=COM_Mask_PointSrc_sevem_dx11d_70_pol_varhole_full_99p90_1024_R2.00.fits}} (clean 70 GHz);<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_dx11d_100_pol_99.97pc_radius_3sigma_10arcmin_1024_R2.00.fits|link=COM_Mask_PointSrc_sevem_dx11d_100_pol_99.97pc_radius_3sigma_10arcmin_1024_R2.00.fits}} (clean 100 GHz)<br />
*{{PLASingleFile|fileType=map|name=COM_Mask_PointSrc_sevem_dx11d_143_pol_99.97pc_radius_3sigma_10arcmin_1024_R2.00.fits|link=COM_Mask_PointSrc_sevem_dx11d_143_pol_99.97pc_radius_3sigma_10arcmin_1024_R2.00.fits}} (clean 143 GHz)<br />
|- bgcolor="800000"<br />
|<br />
! ||<br />
|<br />
|- bgcolor="ffdead" <br />
! NILC 2015 (PR2) || Used for diffuse inpainting of input frequency maps || Used for constrained Gaussian realization inpaiting of CMB map || Description<br />
|-<br />
|TMASK || NO || NO || TMASK (the equivalent to PR1 VALMASK) is the confidence mask in temperature that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-nilc-field-Int_2048_R2.01_full.fits|link=COM_CMB_IQU-nilc-field-Int_2048_R2.01_full.fits}}.<br />
|-<br />
|PMASK || NO || NO || PMASK is the confidence mask in polarization that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-nilc_1024_R2.02_full.fits|link=COM_CMB_IQU-nilc_1024_R2.02_full.fits}}.<br />
|-<br />
|INP_MASK || YES || NO || The pre-processing involves inpainting of the holes in INP_MASK in the frequency maps prior to applying NILC on them. The first mask (nside 2048) has been used for the pre-processing of sky maps for HFI channels and second one for LFI channels (nside 1024). They can downloaded here:<br />
{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_nilc_dx11_preproc_1024_R2.00.fits|link=COM_Mask_PointSrcGalplane_nilc_dx11_preproc_1024_R2.00.fits}}<br />
{{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_nilc_dx11_preproc_2048_R2.00.fits|link=COM_Mask_PointSrcGalplane_nilc_dx11_preproc_2048_R2.00.fits}} <br />
|- bgcolor="800000"<br />
|<br />
! ||<br />
|<br />
|- bgcolor="ffdead" <br />
! SMICA 2015 (PR2) || Used for diffuse inpainting of input frequency maps || Used for constrained Gaussian realization inpaiting of CMB map || Description<br />
|-<br />
|TMASK || NO || YES || TMASK (the equivalent to PR1 VALMASK) is the confidence mask in temperature that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-smica-field-Int_2048_R2.01_full.fits|link=COM_CMB_IQU-smica-field-Int_2048_R2.01_full.fits}}.<br />
|-<br />
|PMASK || NO || YES || PMASK is the confidence mask in polarization that defines the region where the reconstructed CMB is trusted. It can be found inside {{PLASingleFile|fileType=map|name=COM_CMB_IQU-smica_1024_R2.02_full.fits|link=COM_CMB_IQU-smica_1024_R2.02_full.fits}}.<br />
|-<br />
|I_MASK || YES || NO || I_MASK, as in PR1, defines the regions over which CMB is not built. It is a combination of point source masks, Galactic plane mask and other bright regions like LMC, SMC, etc. It can downloaded here: {{PLASingleFile|fileType=map|name=COM_Mask_PointSrcGalplane_smica_harmonic_mask_2048_R2.00.fits|link=COM_Mask_PointSrcGalplane_smica_harmonic_mask_2048_R2.00.fits}}<br />
|- <br />
|}<br />
<br />
===Inputs===<br />
The input maps are the sky temperature maps described in the [[Frequency Maps | Sky temperature maps]] section. SMICA and SEVEM use all the maps between 30 and 857 GHz; NILC uses the ones between 44 and 857 GHz. Commander-Ruler uses frequency channel maps from 30 to 353 GHz. <br />
<br />
===File names and structure===<br />
<br />
Three sets of files FITS files containing the CMB products are available. In the first set all maps (i.e., covering different parts of the mission) and all characterisation products for a given method and a given Stokes parameter are grouped into a single extension, and there are two files per ''method'' (smica, nilc, sevem, and commander), one for the high resolution data (I only, Nside=2048) and one for low resolution data (Q and U only, Nside=1024). Each file also contains the associated confidence mask(s) and beam transfer function. '''These are the R2.00 files''' which have names like<br />
*''COM_CMB_IQU-{method}-field-{Int,Pol}_Nside_R2.00.fits''<br />
There are 7 coverage periods:''full'', ''halfyear-1,2'', ''halfmission-1,2'', or ''ringhalf-1,2'', and 4 characterisation products: ''half-sum'' and half-difference'' for the year and the half-mission periods.<br />
<br />
In the second second set the different coverages are split into different files which in most cases have a single extension with I only (Nside=1024) and I, Q, and U (Nside=1024). This second set was built in order to allow users to use standard codes like ''spice'' or ''anafast'' on them, directly. So this set contains the I maps at Nside=1024, which are not contained in the R2.00; on the other hand this set does not contain the half-sum and half-difference maps. '''These are the 2.01 files''' which have names like <br />
*''COM_CMB_IQU-{method}{-field-Int|Pol}_Nside_R2.01_{coverage}.fits'' for the regular CMB maps, and <br />
*''COM_CMB_IQU-{fff}-{fgsub-sevem}{-field-Int|Pol}_Nside_R2.01_{coverage}.fits'' for the sevem frequency-dependent, foregrounds-subtracted maps,<br />
where ''field-Int|Pol'' is used to indicate that only Int or only Pol data are contained (at present only ''field-Int'' is used for the high-res data), and is not included in the low-res data which contains all three Stokes parameters, and ''coverage'' is one of ''full'', ''halfyear-1,2'', ''halfmission-1,2'', or ''ringhalf-1,2''. Also, the coverage=''full'' files contain also the confidence mask(s) and beam transfer function(s) which are valid for all products of the same method (one for Int and one for Pol when both are available). <br />
<br />
The third set has the same structure as the Nside=1024 products of R2.01, but '''the Q and U maps have been high-pass filtered to remove modes at l < 30 for the reasons indicated earlier. These are the default products for use in polarisation studies. They are the R2.02 files''' which have names like:<br />
*''COM_CMB_IQU-{method}_1024_R2.02_{coverage}.fits'' <br />
<br />
====Version 2.00 files====<br />
<br />
These have names like <br />
*''COM_CMB_IQU-{method}-field-{Int,Pol}_Nside_R2.00.fits'', <br />
as indicated above. They contain:<br />
* a minimal primary extension with no data;<br />
* one or two ''BINTABLE'' data extensions with a table of Npix lines by 14 columns in which the first 13 columns is a CMB maps produced from the full or a subset of the data, as described in the table below, and the last column in a confidence mask. There is a single extension for ''Int'' files, and two, for Q and U, for ''Pol'' files. <br />
* a ''BINTABLE'' extension containing the beam transfer function (mistakenly called window function in the files).<br />
<br />
If Nside=1024 the files contain I, Q and U maps, whereas if Nside=2048 only the I map is given.<br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''CMB R2.00 map file data structure'''<br />
|- bgcolor="ffdead" <br />
! colspan="4" | Ext. 1. or 2. EXTNAME = ''COMP-MAP'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I or Q or U || Real*4 || uK_cmb || I or Q or U map <br />
|- <br />
|HM1 || Real*4 || uK_cmb || Half-miss 1 <br />
|-<br />
|HM2 || Real*4 || uK_cmb || Half-miss 2 <br />
|-<br />
|YR1 || Real*4 || uK_cmb || Year 1 <br />
|-<br />
|YR2 || Real*4 || uK_cmb || Year 2 <br />
|-<br />
|HR1 || Real*4 || uK_cmb || Half-ring 1 <br />
|-<br />
|HR2 || Real*4 || uK_cmb || Half-ring 2 <br />
|-<br />
|HMHS || Real*4 || uK_cmb || Half-miss, half sum <br />
|-<br />
|HMHD || Real*4 || uK_cmb || Half-miss, half diff <br />
|-<br />
|YRHS || Real*4 || uK_cmb || Year, half sum <br />
|-<br />
|YRHD || Real*4 || uK_cmb || Year, half diff <br />
|-<br />
|HRHS || Real*4 || uK_cmb || Half-ring half sum <br />
|-<br />
|HRHD || Real*4 || uK_cmb || Half-ring half diff <br />
|-<br />
|MASK || BYTE || || Confidence mask <br />
|-<br />
<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || String || CMB || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|POLCCONV || String || COSMO || Polarization convention<br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 1024 or 2048 || Healpix Nside<br />
|-<br />
|METHOD || String ||name || Cleaning method (smica/nilc/sevem/commander)<br />
|-<br />
|- bgcolor="ffdead" <br />
!colspan="4" | Ext. 2. or 3. EXTNAME = ''BEAM_WF'' (BINTABLE) . See Note 1<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|BEAMWF || Real*4 || none || The effective beam transfer function, including the pixel window function. See Note 2.<br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|LMIN || Int || value || First multipole of beam TF<br />
|-<br />
|LMAX || Int || value || Last multipole of beam TF<br />
|-<br />
|METHOD || String ||name || Cleaning method (SMICA/NILC/SEVEM/COMMANDER-Ruler)<br />
|-<br />
|}<br />
<br />
Notes:<br />
# Actually this is a beam ''transfer'' function, so BEAM_TF would have been more appropriate.<br />
# The beam transfer function <math>B_\ell</math> given here includes the pixel window function <math>p_\ell</math> for the Nside=2048 pixelization. It means that, ideally, <math>C_\ell(map) = C_\ell(sky) \, B_\ell^2 \, p_\ell^2</math>. The beam ''Window'' function is given by <math>W_\ell = B_\ell^2</math><br />
<br />
<br />
====Version 2.01 files====<br />
<br />
These files have names like:<br />
*''COM_CMB_IQU-{method}{-field-Int|Pol}_Nside_R2.01_{coverage}.fits'' for the regular CMB maps, and <br />
*''COM_CMB_IQU-{fff}-{fgsub-sevem}{-field-Int|Pol}_Nside_R2.01_{coverage}.fits'' for the sevem frequency-dependent, foregrounds-subtracted maps,<br />
as indicated above. They contain:<br />
* a minimal primary extension with no data, but with a NUMEXT keyword giving the number of extensions contained.<br />
* one or two ''BINTABLE'' data extensions with a table of Npix lines by 1-5 columns depending on the file, as described above: the minimum begin I only, the maximum begin I, Q, U, and confidence masks for I and P. <br />
* a ''BINTABLE'' extension containing the beam transfer function(s): one for I, and a second one that applies to both Q and U, if Nslde=1024.<br />
<br />
If Nside=1024 the files contain I, Q and U maps, whereas if Nside=2048 only the I map is given. The basic structure, including information on the most important keywords, is given in the table below. For full details, see the FITS header.<br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''CMB R2.01 map file data structure'''<br />
|- bgcolor="ffdead" <br />
! colspan="4" | Ext. 1. or 2. EXTNAME = ''COMP-MAP'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_Stokes || Real*4 || uK_cmb || I map (Nside=1024,2048) <br />
|- <br />
|Q_Stokes || Real*4 || uK_cmb || Q map (Nside=1024) <br />
|-<br />
|U_Stokes || Real*4 || uK_cmb || U map (Nside=2048) <br />
|-<br />
|TMASK || Int || none || optional Temperature confidence mask <br />
|-<br />
|PMASK || Int || none || optional Polarisation confidence mask <br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || String || CMB || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|POLCCONV || String || COSMO || Polarization convention<br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 1024 or 2048 || Healpix Nside<br />
|-<br />
|METHOD || String ||name || Cleaning method (SMICA/NILC/SEVEM)<br />
|-<br />
|- bgcolor="ffdead" <br />
!colspan="4" | Optional Ext. 2. or 3. EXTNAME = ''BEAM_TF'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|INT_BEAM || Real*4 || none || Effective beam transfer function. See Note 1.<br />
|-<br />
|POL_BEAM || Real*4 || none || Effective beam transfer function. See Note 1.<br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|LMIN || Int || value || First multipole of beam WF<br />
|-<br />
|LMAX_I || Int || value || Last multipole for Int beam TF<br />
|-<br />
|LMAX_P || Int || value || Last multipole for Pol beam TF<br />
|-<br />
|METHOD || String ||name || Cleaning method <br />
|-<br />
|}<br />
Notes:<br />
# The beam transfer function <math>B_\ell</math> given here includes the pixel window function <math>p_\ell</math> for the Nside=2048 pixelization. It means that, ideally, <math>C_\ell(map) = C_\ell(sky) \, B_\ell^2 \, p_\ell^2</math>. The beam ''Window'' function is given by <math>W_\ell = B_\ell^2</math><br />
<br />
====Version 2.02 files====<br />
<br />
'''For polarisation work, this is the default set of files to be used for cosmological analysis. Their content is identical to the "R2.01" files, except that angular scales above l < 30 have been filtered out of the Q and U maps. '''<br />
<br />
These files have names like:<br />
*''COM_CMB_IQU-{method}_1024_R2.02_{coverage}.fits'' <br />
as indicated above. They contain:<br />
The files contain <br />
* a minimal primary extension with no data, but with a NUMEXT keyword giving the number of extensions contained.<br />
* one or two ''BINTABLE'' data extensions with a table of Npix lines by 1-5 columns depending on the file, as described above: the minimum begin I only, the maximum begin I, Q, U, and confidence masks for I and P. <br />
* a ''BINTABLE'' extension containing 2 beam transfer functions: one for I and one that applies to both Q and U.<br />
<br />
If Nside=1024 the files contain I, Q and U maps, whereas if Nside=2048 only the I map is given. The basic structure, including information on the most important keywords, is given in the table below. For full details, see the FITS header.<br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''CMB R2.02 map file data structure'''<br />
|- bgcolor="ffdead" <br />
! colspan="4" | Ext. 1. or 2. EXTNAME = ''COMP-MAP'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_Stokes || Real*4 || uK_cmb || I map (Nside=1024) <br />
|- <br />
|Q_Stokes || Real*4 || uK_cmb || Q map (Nside=1024) <br />
|-<br />
|U_Stokes || Real*4 || uK_cmb || U map (Nside=2048) <br />
|-<br />
|TMASK || Int || none || optional Temperature confidence mask <br />
|-<br />
|PMASK || Int || none || optional Polarisation confidence mask <br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || String || CMB || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|POLCCONV || String || COSMO || Polarization convention<br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 1024 or 2048 || Healpix Nside<br />
|-<br />
|METHOD || String ||name || Cleaning method (SMICA/NILC/SEVEM)<br />
|-<br />
|- bgcolor="ffdead" <br />
!colspan="4" | Optional Ext. 2. or 3. EXTNAME = ''BEAM_TF'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|INT_BEAM || Real*4 || none || Effective beam transfer function. See Note 1.<br />
|-<br />
|POL_BEAM || Real*4 || none || Effective beam transfer function. See Note 1.<br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|LMIN || Int || value || First multipole of beam WF<br />
|-<br />
|LMAX_I || Int || value || Last multipole for Int beam TF<br />
|-<br />
|LMAX_P || Int || value || Last multipole for Pol beam TF<br />
|-<br />
|METHOD || String ||name || Cleaning method <br />
|-<br />
|}<br />
Notes:<br />
# The beam transfer function <math>B_\ell</math> given here includes the pixel window function <math>p_\ell</math> for the Nside=2048 pixelization. It means that, ideally, <math>C_\ell(map) = C_\ell(sky) \, B_\ell^2 \, p_\ell^2</math>. The beam ''Window'' function is given by <math>W_\ell = B_\ell^2</math><br />
<br />
<br />
<br />
====The Common Masks====<br />
<br />
The common masks are stored into two different files for Temperature and Polarisation respectively:<br />
* ''COM_CMB_IQU-common-field-MaskInt_2048_R2.nn.fits'' with the UT78 and UTA76 masks<br />
* ''COM_CMB_IQU-common-field-MaskPol_1024_R2.nn.fits'' with the UP78, UPA77, and UPB77 masks<br />
Both files contain also a map of the missing pixels for the half mission and year coverage periods. The 2 (for Temp) or 3 (for Pol) masks and the missing pixels maps are stored in 4 or 5 column a ''BINTABLE'' extension 1 of each file, named ''MASK-INT'' and ''MASK-POL'', respectively. See the FITS file headers for details.<br />
<br />
====Quadrupole residual maps====<br />
<br />
The quadrupole residual maps are stored in files called:<br />
* ''COM_CMB_IQU-kq-resid-{method}-field-Int_2048_R2.02.fits''<br />
<br />
They contain:<br />
* a minimal primary extension with no data, but with a NUMEXT keyword giving the number of extensions contained.<br />
* a single ''BINTABLE'' extension with a single column of Npix lines containing the HEALPIX map indicated<br />
<br />
The basic structure of the data extension is shown below. For full details see the extension header. <br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''Kinetic quadrupole residual map file data structure'''<br />
|- bgcolor="ffdead" <br />
! colspan="4" | Ext. 1. EXTNAME = ''COMP-MAP'' (BINTABLE)<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|INTENSITY || Real*4 || K_cmb || the residual map <br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || String || KQ-RESID || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|POLCCONV || String || COSMO || Polarization convention<br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 2048 || Healpix Nside<br />
|-<br />
|METHOD || String ||name || Cleaning method<br />
|-<br />
|}<br />
<br />
== Astrophysical foregrounds from parametric component separation ==<br />
We describe diffuse foreground products for the Planck 2015 release. See the Planck Foregrounds Component Separation paper {{PlanckPapers|planck2014-a12}} for a detailed description of these products. Further scientific discussion and interpretation may be found in {{PlanckPapers|planck2014-a31}}.<br />
<br />
===Low-resolution temperature products===<br />
<br />
: The Planck 2015 astrophysical component separation analysis combines Planck observations with the 9-year WMAP temperature sky maps (Bennett et al. 2013) and the 408 MHz survey by Haslam et al. (1982). This allows a direct decomposition of the low-frequency foregrounds into separate synchrotron, free-free and spinning dust components without strong spatial priors. <br />
<br />
====Inputs====<br />
<br />
The following data products are used for the low-resolution analysis:<br />
* Full-mission 30 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=30|period=Full|link=LFI 30 GHz frequency maps}}<br />
* Full-mission 44 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=44|period=Full|link=LFI 44 GHz frequency maps}}<br />
* Full-mission 70 GHz ds1 (18+23), ds2 (19+22), and ds3 (20+21) detector-set maps<br />
* Full-mission 100 GHz ds1 and ds2 detector set maps<br />
* Full-mission 143 GHz ds1 and ds2 detector set maps and detectors 5, 6, and 7 maps<br />
* Full-mission 217 GHz detector 1, 2, 3 and 4 maps<br />
* Full-mission 353 GHz detector set ds2 and detector 1 maps<br />
* Full-mission 545 GHz detector 2 and 4 maps<br />
* Full-mission 857 GHz detector 2 map<br />
* Beam-symmetrized 9-year WMAP K-band map [http://lambda.gsfc.nasa.gov/product/map/dr5/skymap_info.cfm (Lambda)]<br />
* Beam-symmetrized 9-year WMAP Ka-band map [http://lambda.gsfc.nasa.gov/product/map/dr5/skymap_info.cfm (Lambda)]<br />
* Default 9-year WMAP Q1 and Q2 differencing assembly maps [http://lambda.gsfc.nasa.gov/product/map/dr5/skymap_info.cfm (Lambda)]<br />
* Default 9-year WMAP V1 and V2 differencing assembly maps [http://lambda.gsfc.nasa.gov/product/map/dr5/skymap_info.cfm (Lambda)]<br />
* Default 9-year WMAP W1, W2, W3, and W4 differencing assembly maps [http://lambda.gsfc.nasa.gov/product/map/dr5/skymap_info.cfm (Lambda)]<br />
* Re-processed 408 MHz survey map, Remazeilles et al. (2014) [http://lambda.gsfc.nasa.gov/product/foreground/2014_haslam_408_info.cfm (Lambda)]<br />
All maps are smoothed to a common resolution of 1 degree FWHM by deconvolving their original instrumental beam and pixel window, and convolving with the new common Gaussian beam, and repixelizing at Nside=256.<br />
<br />
====Outputs====<br />
<br />
=====Synchrotron emission=====<br />
<br />
<!--<center><br />
<gallery style="padding:0 0 0 0;" perrow=3 widths=800px heights=500px> <br />
File:commander_synch_amp.png | '''Commander low-resolution synchrotron amplitude'''<br />
</gallery><br />
</center>--><br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_Synchrotron-commander_0256_R2.00.fits|link=COM_CompMap_Synchrotron-commander_0256_R2.00.fits}}<br />
: Reference frequency: 408 MHz<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-Synchrotron<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || uK_RJ || Amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || uK_RJ || Amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || uK_RJ || Amplitude posterior rms<br />
|}<br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ Extension 1 -- SYNC-TEMP<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|nu || Real*4 || Hz || Frequency <br />
|-<br />
|intensity || Real*4 || W/Hz/m2/sr || GALPROP z10LMPD_SUNfE spectrum <br />
|}<br />
<br />
=====Free-free emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_freefree-commander_0256_R2.00.fits|link=COM_CompMap_freefree-commander_0256_R2.00.fits}}<br />
: Reference frequency: NA<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-freefree<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|EM_ML || Real*4 || cm^-6 pc || Emission measure posterior maximum <br />
|-<br />
|EM_MEAN || Real*4 || cm^-6 pc || Emission measure posterior mean <br />
|-<br />
|EM_RMS || Real*4 || cm^-6 pc || Emission measure posterior rms<br />
|-<br />
|TEMP_ML || Real*4 || K || Electron temperature posterior maximum <br />
|-<br />
|TEMP_MEAN || Real*4 || K || Electron temperature posterior mean <br />
|-<br />
|TEMP_RMS || Real*4 || K || Electron temperature posterior rms<br />
|}<br />
<br />
<br />
=====Spinning dust emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_AME-commander_0256_R2.00.fits|link=COM_CompMap_AME-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
Note: The spinning dust component has two independent constituents, each corresponding to one spdust2 component, but with different peak frequencies. The two components are stored in the two first FITS extensions, and the template frequency spectrum is stored in the third extension. <br />
<br />
: Reference frequency: 22.8 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-AME1<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || uK_RJ || Primary amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || uK_RJ || Primary amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || uK_RJ || Primary amplitude posterior rms<br />
|-<br />
|FREQ_ML || Real*4 || GHz || Primary peak frequency posterior maximum <br />
|-<br />
|FREQ_MEAN || Real*4 || GHz || Primary peak frequency posterior mean <br />
|-<br />
|FREQ_RMS || Real*4 || GHz || Primary peak frequency posterior rms<br />
|}<br />
<br />
: Reference frequency: 41.0 GHz<br />
: Peak frequency: 33.35 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ Extension 1 -- COMP-MAP-AME2<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || uK_RJ || Secondary amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || uK_RJ || Secondary amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || uK_RJ || Secondary amplitude posterior rms<br />
|}<br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ Extension 2 -- SPINNING-DUST-TEMP<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|nu || Real*4 || GHz || Frequency <br />
|-<br />
|j_nu/nH || Real*4 || Jy sr-1 cm2/H || spdust2 spectrum <br />
|}<br />
<br />
=====CO line emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_CO-commander_0256_R2.00.fits|link=COM_CompMap_CO-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
Note: The CO line emission component has three independent objects, corresponding to the J1->0, 2->1 and 3->2 lines, stored in separate extensions. <br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-co10<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || K_RJ km/s || CO(1-0) amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || K_RJ km/s || CO(1-0) amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || K_RJ km/s || CO(1-0) amplitude posterior rms<br />
|}<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ Extension 1 -- COMP-MAP-co21<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || K_RJ km/s || CO(2-1) amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || K_RJ km/s || CO(2-1) amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || K_RJ km/s || CO(2-1) amplitude posterior rms<br />
|}<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ Extension 2 -- COMP-MAP-co32<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || K_RJ km/s || CO(3-2) amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || K_RJ km/s || CO(3-2) amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || K_RJ km/s || CO(3-2) amplitude posterior rms<br />
|}<br />
<br />
=====94/100 GHz line emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_xline-commander_0256_R2.00.fits|link=COM_CompMap_xline-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-xline<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || uK_cmb || Amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || uK_cmb || Amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || uK_cmb || Amplitude posterior rms<br />
|}<br />
<br />
Note: The amplitude of this component is normalized according to the 100-ds1 detector set map, ie., it is the amplitude as measured by this detector combination.<br />
<br />
=====Thermal dust emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_dust-commander_0256_R2.00.fits|link=COM_CompMap_dust-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
: Reference frequency: 545 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-dust<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML || Real*4 || uK_RJ || Amplitude posterior maximum <br />
|-<br />
|I_MEAN || Real*4 || uK_RJ || Amplitude posterior mean <br />
|-<br />
|I_RMS || Real*4 || uK_RJ || Amplitude posterior rms<br />
|-<br />
|TEMP_ML || Real*4 || K || Dust temperature posterior maximum <br />
|-<br />
|TEMP_MEAN || Real*4 || K || Dust temperature posterior mean <br />
|-<br />
|TEMP_RMS || Real*4 || K || Dust temperature posterior rms<br />
|-<br />
|BETA_ML || Real*4 || NA || Emissivity index posterior maximum <br />
|-<br />
|BETA_MEAN || Real*4 || NA || Emissivity index posterior mean <br />
|-<br />
|BETA_RMS || Real*4 || NA || Emissivity index posterior rms<br />
|}<br />
<br />
=====Thermal Sunyaev-Zeldovich emission around the Coma and Virgo clusters=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_SZ-commander_0256_R2.00.fits|link=COM_CompMap_SZ-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 60 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-SZ<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Y_ML || Real*4 || y_SZ || Y parameter posterior maximum <br />
|-<br />
|Y_MEAN || Real*4 || y_SZ || Y parameter posterior mean <br />
|-<br />
|Y_RMS || Real*4 || y_SZ || Y parameter posterior rms<br />
|}<br />
<br />
===High-resolution temperature products===<br />
<br />
High-resolution foreground products at 7.5 arcmin FWHM are derived with the same algorithm as for the low-resolution analyses, but including frequency channels above (and including) 143 GHz. <br />
<br />
====Inputs====<br />
<br />
The following data products are used for the low-resolution analysis:<br />
* Full-mission 143 GHz ds1 and ds2 detector set maps and detectors 5, 6, and 7 maps<br />
* Full-mission 217 GHz detector 1, 2, 3 and 4 maps<br />
* Full-mission 353 GHz detector set ds2 and detector 1 maps<br />
* Full-mission 545 GHz detector 2 and 4 maps<br />
* Full-mission 857 GHz detector 2 map<br />
All maps are smoothed to a common resolution of 7.5 arcmin FWHM by deconvolving their original instrumental beam and pixel window, and convolving with the new common Gaussian beam, and repixelizing at Nside=2048.<br />
<br />
====Outputs====<br />
<br />
=====CO J2->1 emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_CO21-commander_2048_R2.00.fits|link=COM_CompMap_CO21-commander_2048_R2.00.fits}}<br />
: Nside = 2048<br />
: Angular resolution = 7.5 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-CO21<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML_FULL || Real*4 || K_RJ km/s || Full-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HM1 || Real*4 || K_RJ km/s || First half-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HM2 || Real*4 || K_RJ km/s || Second half-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HR1 || Real*4 || K_RJ km/s || First half-ring amplitude posterior maximum <br />
|-<br />
|I_ML_HR2 || Real*4 || K_RJ km/s || Second half-ring amplitude posterior maximum <br />
|-<br />
|I_ML_YR1 || Real*4 || K_RJ km/s || "First year" amplitude posterior maximum <br />
|-<br />
|I_ML_YR2 || Real*4 || K_RJ km/s || "Second year" amplitude posterior maximum <br />
|}<br />
<br />
<br />
=====Thermal dust emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_ThermalDust-commander_2048_R2.00.fits|link=COM_CompMap_ThermalDust-commander_2048_R2.00.fits}}<br />
: Nside = 2048<br />
: Angular resolution = 7.5 arcmin<br />
<br />
: Reference frequency: 545 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-dust<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I_ML_FULL || Real*4 || uK_RJ || Full-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HM1 || Real*4 || uK_RJ || First half-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HM2 || Real*4 || uK_RJ || Second half-mission amplitude posterior maximum <br />
|-<br />
|I_ML_HR1 || Real*4 || uK_RJ || First half-ring amplitude posterior maximum <br />
|-<br />
|I_ML_HR2 || Real*4 || uK_RJ || Second half-ring amplitude posterior maximum <br />
|-<br />
|I_ML_YR1 || Real*4 || uK_RJ || "First year" amplitude posterior maximum <br />
|-<br />
|I_ML_YR2 || Real*4 || uK_RJ || "Second year" amplitude posterior maximum <br />
|-<br />
|BETA_ML_FULL || Real*4 || NA || Full-mission emissivity index posterior maximum <br />
|-<br />
|BETA_ML_HM1 || Real*4 || NA || First half-mission emissivity index posterior maximum <br />
|-<br />
|BETA_ML_HM2 || Real*4 || NA || Second half-mission emissivity index posterior maximum <br />
|-<br />
|BETA_ML_HR1 || Real*4 || NA || First half-ring emissivity index posterior maximum <br />
|-<br />
|BETA_ML_HR2 || Real*4 || NA || Second half-ring emissivity index posterior maximum <br />
|-<br />
|BETA_ML_YR1 || Real*4 || NA || "First year" emissivity index posterior maximum <br />
|-<br />
|BETA_ML_YR2 || Real*4 || NA || "Second year" emissivity index posterior maximum <br />
|-<br />
|}<br />
<br />
===Polarization products===<br />
<br />
Two polarization foreground products are provided, namely synchrotron and thermal dust emission. The spectral models are assumed identical to the corresponding temperature spectral models.<br />
<br />
====Inputs====<br />
<br />
The following data products are used for the polarization analysis:<br />
* (Only low-resolution analysis) Full-mission 30 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=30|period=Full|link=LFI 30 GHz frequency maps}}<br />
* (Only low-resolution analysis) Full-mission 44 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=44|period=Full|link=LFI 44 GHz frequency maps}}<br />
* (Only low-resolution analysis) Full-mission 70 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=70|period=Full|link=LFI 70 GHz frequency maps}}<br />
* Full-mission 100 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=100|period=Full|link=HFI 100 GHz frequency maps}}<br />
* Full-mission 143 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=143|period=Full|link=HFI 143 GHz frequency maps}}<br />
* Full-mission 217 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=217|period=Full|link=HFI 217 GHz frequency maps}}<br />
* Full-mission 353 GHz frequency map, {{PLAFreqMaps|inst=LFI|freq=353|period=Full|link=HFI 353 GHz frequency maps}}<br />
In the low-resolution analysis, all maps are smoothed to a common resolution of 40 arcmin FWHM by deconvolving their original instrumental beam and pixel window, and convolving with the new common Gaussian beam, and repixelizing at Nside=256. In the high-resolution analysis (including only CMB and thermal dust emission), the corresponding resolution is 10 arcmin FWHM and Nside=1024.<br />
<br />
====Outputs====<br />
=====Synchrotron emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits|link=COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits}}<br />
: Nside = 256<br />
: Angular resolution = 40 arcmin<br />
<br />
: Reference frequency: 30 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-SynchrotronPol<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Q_ML_FULL || Real*4 || &mu;K_RJ || Full-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_FULL || Real*4 || &mu;K_RJ || Full-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HM1 || Real*4 || &mu;K_RJ || First half-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_HM1 || Real*4 || &mu;K_RJ || First half-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HM2 || Real*4 || &mu;K_RJ || Second half-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_HM2 || Real*4 || &mu;K_RJ || Second half-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HR1 || Real*4 || &mu;K_RJ || First half-ring Stokes Q posterior maximum <br />
|-<br />
|U_ML_HR1 || Real*4 || &mu;K_RJ || First half-ring Stokes U posterior maximum <br />
|-<br />
|Q_ML_HR2 || Real*4 || &mu;K_RJ || Second half-ring Stokes Q posterior maximum <br />
|-<br />
|U_ML_HR2 || Real*4 || &mu;K_RJ || Second half-ring Stokes U posterior maximum <br />
|-<br />
|Q_ML_YR1 || Real*4 || &mu;K_RJ || "First year" Stokes Q posterior maximum <br />
|-<br />
|U_ML_YR1 || Real*4 || &mu;K_RJ || "First year" Stokes U posterior maximum <br />
|-<br />
|Q_ML_YR2 || Real*4 || &mu;K_RJ || "Second year" Stokes Q posterior maximum <br />
|-<br />
|U_ML_YR2 || Real*4 || &mu;K_RJ || "Second year" Stokes U posterior maximum <br />
|}<br />
<br />
=====Thermal dust emission=====<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_DustPol-commander_1024_R2.00.fits|link=COM_CompMap_DustPol-commander_1024_R2.00.fits}}<br />
: Nside = 1024<br />
: Angular resolution = 10 arcmin<br />
<br />
: Reference frequency: 353 GHz<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-DustPol<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Q_ML_FULL || Real*4 || uK_RJ || Full-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_FULL || Real*4 || uK_RJ || Full-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HM1 || Real*4 || uK_RJ || First half-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_HM1 || Real*4 || uK_RJ || First half-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HM2 || Real*4 || uK_RJ || Second half-mission Stokes Q posterior maximum <br />
|-<br />
|U_ML_HM2 || Real*4 || uK_RJ || Second half-mission Stokes U posterior maximum <br />
|-<br />
|Q_ML_HR1 || Real*4 || uK_RJ || First half-ring Stokes Q posterior maximum <br />
|-<br />
|U_ML_HR1 || Real*4 || uK_RJ || First half-ring Stokes U posterior maximum <br />
|-<br />
|Q_ML_HR2 || Real*4 || uK_RJ || Second half-ring Stokes Q posterior maximum <br />
|-<br />
|U_ML_HR2 || Real*4 || uK_RJ || Second half-ring Stokes U posterior maximum <br />
|-<br />
|Q_ML_YR1 || Real*4 || uK_RJ || "First year" Stokes Q posterior maximum <br />
|-<br />
|U_ML_YR1 || Real*4 || uK_RJ || "First year" Stokes U posterior maximum <br />
|-<br />
|Q_ML_YR2 || Real*4 || uK_RJ || "Second year" Stokes Q posterior maximum <br />
|-<br />
|U_ML_YR2 || Real*4 || uK_RJ || "Second year" Stokes U posterior maximum <br />
|}<br />
<br />
== CO emission maps ==<br />
<br />
CO rotational transition line emission is present in all HFI bands except for the 143 GHz channel. It is especially significant in the 100, 217 and 353 GHz channels (due to the 115 (1-0), 230 (2-1) and 345 GHz (3-2) CO transitions). This emission comes essentially from the Galactic interstellar medium and is mainly located at low and intermediate Galactic latitudes. Three approaches (summarised below) have been used to extract CO velocity-integrated emission maps from HFI maps and to make three types of CO products. A full description of how these products were generated is given in {{PlanckPapers|planck2013-p03a}} and {{PlanckPapers|planck2014-a12}}.<br />
<br />
* Type 1 product: it is based on a single channel approach using the fact that each CO line has a slightly different transmission in each bolometer at a given frequency channel. These transmissions can be evaluated from bandpass measurements that were performed on the ground or empirically determined from the sky using existing ground-based CO surveys. From these, the J=1-0, J=2-1 and J=3-2 CO lines can be extracted independently. As this approach is based on individual bolometer maps of a single channel, the resulting Signal-to-Noise ratio (SNR) is relatively low. The benefit, however, is that these maps do not suffer from contamination from other HFI channels (as is the case for the other approaches) and are more reliable, especially in the Galactic Plane. The improvement relative to the 2013 release comes from the combined effect of the ADC correction, the VLTC correction, and the improved calibration scheme. As a result, the noise level is ~30% lower in the new products, and the maps are much better behaved at high latitudes.<br />
<br />
* Type 2 product: this product is obtained using a multi frequency approach. Three frequency channel maps are combined to extract the J=1-0 (using the 100, 143 and 353 GHz channels) and J=2-1 (using the 143, 217 and 353 GHz channels) CO maps. Because frequency channels are combined, the spectral behaviour of other foregrounds influences the result. The two type 2 CO maps produced in this way have a higher SNR than the type 1 maps at the cost of a larger possible residual contamination from other diffuse foregrounds.<br />
* Type 3 product: to generate this product, fixed CO line ratios are assumed and a high-resolution parametric foreground model is fit. In 2013 this product was generated using the Commander-Ruler technique. In 2015, this technique is superseded by the high-resolution Commander-only, used to produce the J=2-1 map presented in [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps#CO_J2-.3E1_emission] and described in Section 5.4 of {{PlanckPapers|planck2014-a12}}.<br />
<br />
Type 1 and 2 maps have been produced using the MILCA algorithm. Commander has been used to produce low resolution CO J=1-0,2-1,3-2 maps ([[CMB_and_astrophysical_component_maps#CO_line_emission|here]]) and high resolution CO J=2-1 maps ([[CMB_and_astrophysical_component_maps#CO_J2-.3E1_emission|here]]).<br />
<br />
A summary of all the 2015 CO maps can be found in Table 9 from {{PlanckPapers|planck2014-a12}}, also shown here:<br />
<br />
[[File:Planck_2015_A10_Fig9_CO_maps.png]]<br />
<br />
<br />
Characteristics of the released maps are the following. We provide Healpix maps with Nside=2048. For one transition, the CO velocity-integrated line signal map is given in K_RJ.km/s units. A conversion factor from this unit to the native unit of HFI maps (K_CMB) is provided in the header of the data files and in the RIMO. Four maps are given per transition and per type:<br />
* The signal map<br />
* The standard deviation map (same unit as the signal), <br />
* A null test noise map (same unit as the signal) with similar statistical properties. It is made out of half the difference of half-ring maps.<br />
* A mask map (0 or 1) giving the regions (1) where the CO measurement is not reliable because of some severe identified foreground contamination.<br />
<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=HFI_CompMap_CO-Type1_2048_R2.00.fits|link=HFI_CompMap_CO-Type1_2048_R2.00.fits}}<br />
: Nside = 2048<br />
<br />
<br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''Type-1 CO map file data structure'''<br />
|- bgcolor="ffdead" <br />
!colspan="4" | 1. EXTNAME = 'COMP-MAP' <br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|INTEN10 || Real*4 || K_RJ km/sec || The CO(1-0) intensity map<br />
|-<br />
|ERR10 || Real*4 || K_RJ km/sec || Uncertainty in the CO(1-0) intensity<br />
|-<br />
|NULL10 || Real*4 || K_RJ km/sec || Map built from the half-ring difference maps<br />
|-<br />
|MASK10 || Byte || none || Region over which the CO(1-0) intensity is considered reliable<br />
|-<br />
|INTEN21 || Real*4 || K_RJ km/sec || The CO(2-1) intensity map<br />
|-<br />
|ERR21 || Real*4 || K_RJ km/sec || Uncertainty in the CO(2-1) intensity<br />
|-<br />
|NULL21 || Real*4 || K_RJ km/sec || Map built from the half-ring difference maps<br />
|-<br />
|MASK21 || Byte || none || Region over which the CO(2-1) intensity is considered reliable<br />
|-<br />
|INTEN32 || Real*4 || K_RJ km/sec || The CO(3-2) intensity map<br />
|-<br />
|ERR32 || Real*4 || K_RJ km/sec || Uncertainty in the CO(3-2) intensity<br />
|-<br />
|NULL32 || Real*4 || K_RJ km/sec || Map built from the half-ring difference maps<br />
|-<br />
|MASK32 || Byte || none || Region over which the CO(3-2) intensity is considered reliable<br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || string || CO-TYPE1 || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 2048 || Healpix Nside for LFI and HFI, respectively<br />
|-<br />
|FIRSTPIX || Int*4 || 0 || First pixel number<br />
|-<br />
|LASTPIX || Int*4 || 50331647 || Last pixel number, for LFI and HFI, respectively<br />
|-<br />
|CNV 1-0 || Real*4 || value || Factor to convert CO(1-0) intensity to Kcmb (units Kcmb/(Krj*km/s)) <br />
|-<br />
|CNV 2-1 || Real*4 || value || Factor to convert CO(2-1) intensityto Kcmb (units Kcmb/(Krj*km/s)) <br />
|-<br />
|CNV 3-2 || Real*4 || value || Factor to convert CO(3-2) intensityto Kcmb (units Kcmb/(Krj*km/s)) <br />
|}<br />
<br />
<br />
<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=HFI_CompMap_CO-Type2_2048_R2.00.fits|link=HFI_CompMap_CO-Type2_2048_R2.00.fits}}<br />
: Nside = 2048<br />
<br />
<br />
{| border="1" cellpadding="3" cellspacing="0" align="center" style="text-align:left" width=800px<br />
|+ '''Type-2 CO map file data structure'''<br />
|- bgcolor="ffdead" <br />
!colspan="4" | 1. EXTNAME = 'COMP-MAP' <br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|I10 || Real*4 || K_RJ km/sec || The CO(1-0) intensity map<br />
|-<br />
|E10 || Real*4 || K_RJ km/sec || Uncertainty in the CO(1-0) intensity<br />
|-<br />
|N10 || Real*4 || K_RJ km/sec || Map built from the half-ring difference maps<br />
|-<br />
|M10 || Byte || none || Region over which the CO(1-0) intensity is considered reliable<br />
|-<br />
|-<br />
|I21 || Real*4 || K_RJ km/sec || The CO(2-1) intensity map<br />
|-<br />
|E21 || Real*4 || K_RJ km/sec || Uncertainty in the CO(2-1) intensity<br />
|-<br />
|N21 || Real*4 || K_RJ km/sec || Map built from the half-ring difference maps<br />
|-<br />
|M21 || Byte || none || Region over which the CO(2-1) intensity is considered reliable<br />
|-<br />
|- bgcolor="ffdead" <br />
! Keyword || Data Type || Value || Description<br />
|-<br />
|AST-COMP || String || CO-TYPE2 || Astrophysical compoment name<br />
|-<br />
|PIXTYPE || String || HEALPIX ||<br />
|-<br />
|COORDSYS || String || GALACTIC ||Coordinate system <br />
|-<br />
|ORDERING || String || NESTED || Healpix ordering<br />
|-<br />
|NSIDE || Int || 2048 || Healpix Nside for LFI and HFI, respectively<br />
|-<br />
|FIRSTPIX || Int*4 || 0 || First pixel number<br />
|-<br />
|LASTPIX || Int*4 || 50331647 || Last pixel number, for LFI and HFI, respectively<br />
|-<br />
|CNV 1-0 || Real*4 || value || Factor to convert CO(1-0) intensity to Kcmb (units Kcmb/(Krj*km/s)) <br />
|-<br />
|CNV 2-1 || Real*4 || value || Factor to convert CO(2-1) intensityto Kcmb (units Kcmb/(Krj*km/s)) <br />
|}<br />
<br />
== Modelling of the thermal dust emission with the Draine and Li dust model ==<br />
<br />
The Planck, IRAS, and WISE infrared observations were fit with the dust model presented by Draine & Li in 2007 (DL07).<br />
The input maps, the DL07 model, and the fitting procedure and results are presented in {{PlanckPapers|planck2014-XXIX}}. <br />
Here, we describe the input maps and the output maps, which are made available on the Planck Legacy Archive.<br />
<br />
===Inputs===<br />
<br />
The following data have been fit:<br />
<br />
* WISE 12 micron map<br />
* IRAS 60 micron map<br />
* IRAS 100 micron map<br />
* Full-mission 353 GHz PR2 map<br />
* Full-mission 545 GHz PR2 map<br />
* Full-mission 857 GHz PR2 map<br />
<br />
The CIB monopole, the CMB anisotropries and the zodiacal light were subtracted to obtain dust emission maps from the sky emission maps.<br />
All maps were smoothed to a common angular resolution of 5'.<br />
<br />
===Model Parameters===<br />
<br />
For each pixel of the inputs maps, we have fitted four parameters of the DL07 model:<br />
<br />
* the dust mass surface density, Sigma_Mdust, <br />
* the dust mass fraction in small PAH grains, q_PAH, <br />
* the fraction of the total luminosity from dust heated by intense radiation fields, f_PDR,<br />
* the starlight intensity heating the bulk of the dust, U_min.<br />
<br />
The parameter maps and their uncertainties are gathered in one file. This file also includes the <br />
chi2 of the fit per degree of freedom.<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-DL07-Parameters_2048_R2.00.fits|link=COM_CompMap_Dust-DL07-Parameters_2048_R2.00.fits}}<br />
: Nside = 2048<br />
: Angular resolution = 5 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-Dust-DL07-Parameters<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Sigma_Mdust || Real*4 || Solar masses/kpc^2 || Dust mass surface density<br />
|-<br />
|Sigma_Mdust_unc || Real*4 || Solar masses/kpc^2 || Uncertainty (1 sigma) on Sigma_Mdust<br />
|-<br />
|q_PAH || Real*4 || dimensionless || Dust mass fraction in small PAH grains <br />
|-<br />
|q_PAH_unc || Real*4 || dimensionless || Uncertainty (1 sigma) on q_PAH<br />
|-<br />
|f_PDR || Real*4 || dimensionless || Fraction of the total luminosity from dust heated by intense radiation fields<br />
|-<br />
|f_PDR_unc || Real*4 || dimensionless || Uncertainty (1 sigma) on f_PDR<br />
|-<br />
|U_min || Real*4 || dimensionless || Starlight intensity heating the bulk of the dust <br />
|-<br />
|U_min_unc || Real*4 || dimensionless || Uncertainty (1 sigma) on U_min<br />
|-<br />
|Chi2_DOF || Real*4 || dimensionless || Chi2 of the fit per degree of freedom<br />
|}<br />
<br />
===Visible extinction maps===<br />
<br />
We provide two exinctions maps at the visible V band: the value from the model (Av_DL) and the <br />
renormalized one (Av_RQ) that matches extinction estimates for quasars (QSOs) derived from the Sloan digital sky survey (SDSS) data.<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-DL07-AvMaps_2048_R2.00.fits|link=COM_CompMap_Dust-DL07-AvMaps_2048_R2.00.fits}}<br />
: Nside = 2048<br />
: Angular resolution = 5 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-Dust-DL07-AvMaps<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Av_DL || Real*4 || magnitude || Extinction in the V band from the DL model <br />
|-<br />
|Av_DL_unc || Real*4 || magnitude || Uncertainty (1 sigma) on Av_DL<br />
|-<br />
|Av_RQ || Real*4 || magnitude || Extinction in the V band renormalized to match estimates from QSO SDSS observations <br />
|-<br />
|Av_RQ_unc || Real*4 || magnitude || Uncertainty (1 sigma) on Av_RQ<br />
|}<br />
<br />
===Model Fluxes===<br />
<br />
We provide the model predicted fluxes in the following file.<br />
<br />
: File name: {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-DL07-ModelFluxes_2048_R2.00.fits|link=COM_CompMap_Dust-DL07-ModelFluxes_2048_R2.00.fits}}<br />
: Nside = 2048<br />
: Angular resolution = 5 arcmin<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-Dust-DL07-ModelFluxes<br />
|-<br />
|- bgcolor="ffdead" <br />
! Column Name || Data Type || Units || Description<br />
|-<br />
|Planck_857 || Real*4 || MJy/sr || Model flux in the Planck 857 GHz band<br />
|-<br />
|Planck_545 || Real*4 || MJy/sr || Model flux in the Planck 545 GHz band <br />
|-<br />
|Planck_353 || Real*4 || MJy/sr || Model flux in the Planck 353 GHz band<br />
|-<br />
|WISE_12 || Real*4 || MJy/sr || Model flux in the WISE 12 micron band <br />
|-<br />
|IRAS_60 || Real*4 || MJy/sr || Model flux in the IRAS 60 micron band <br />
|-<br />
|IRAS_100 || Real*4 || MJy/sr || Model flux in the IRAS 100 micron band <br />
|}<br />
<br />
<br />
<br />
== Thermal dust and CIB all-sky maps from GNILC component separation ==<br />
We describe diffuse foreground products for the Planck 2015 release produced with the GNILC component separation method. See the Planck paper {{PlanckPapers|planck2016-XLVIII}} for a detailed discussion on these products. <br />
<br />
===Method===<br />
<br />
: The basic idea behind the Generalized Needlet Internal Linear Combination (GNILC) component-separation method ([http://adsabs.harvard.edu/abs/2011MNRAS.418..467R Remazeilles et al, MNRAS 2011]) is to disentangle specific components of emission not on the sole basis of the spectral (frequency) information but also on the basis of their distinct spatial information (angular power spectrum). The GNILC method has been applied to Planck data in order to disentangle Galactic dust emission and Cosmic Infrared Background (CIB) anisotropies. Both components have a similar spectral signature but a distinct angular power spectrum (spatial signature). The spatial information used by GNILC is under the form of priors for the angular power spectra of the CIB, the CMB, and the instrumental noise. No assumption is made on the Galactic signal, neither spectral or spatial. In that sense, GNILC is a blind component-separation method. GNILC operates on a needlet (spherical wavelet) frame, therefore adapting the component separation to the local conditions of contamination both over the sky and over the angular scales.<br />
<br />
===Data===<br />
<br />
: The data used by GNILC for the analysis are the Planck data release 2 (PR2) frequency maps from 30 to 857 GHz, and a 100 micron hybrid map combined from the SFD map ([http://adsabs.harvard.edu/abs/1998ApJ...500..525S Schlegel et al, ApJ 1998]) at large angular scales (> 30') and the IRIS map ([http://adsabs.harvard.edu/abs/2005ApJS..157..302M Miville-Deschênes et al, ApJS 2005]) at small angular scales (< 30'). This special 100 micron map can be obtained in the External Maps section of the PLA.<br />
<br />
===Pre-processing===<br />
<br />
: The point-sources with a signal-to-noise ratio, S/N > 5, in each individual frequency map (30 to 857 GHz, and 100 micron) have been pre-processed by a minimum curvature surface inpainting technique ([http://adsabs.harvard.edu/abs/2015MNRAS.451.4311R Remazeilles et al, MNRAS 2015]) prior to performing component separation with GNILC.<br />
<br />
===GNILC thermal dust and CIB products===<br />
<br />
The result of GNILC component separation are thermal dust and CIB maps at 353, 545, and 857 GHz. In addition, by fitting a modified blackbody model to the GNILC thermal dust products at 353, 545, 857, and 100 micron, we have created all-sky maps of the dust optical depth, dust temperature, and dust emmissivity index. Note that the thermal dust maps have a variable angular resolution over the sky with an effective beam FWHM varying from 21.8' to 5'. The dust beam FWHM map is also released as a product.<br />
<br />
====Thermal dust maps====<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-DUST<br />
|-<br />
|- bgcolor="ffdead" <br />
! File Name || Nside || Units || Reference frequency || Angular resolution || Description<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-F353_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-F353_2048_R2.00.fits}} || 2048 || MJy/sr || 353 GHz || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust amplitude at 353 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-F545_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-F545_2048_R2.00.fits}} || 2048 || MJy/sr || 545 GHz || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust amplitude at 545 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-F857_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-F857_2048_R2.00.fits}} || 2048 || MJy/sr || 857 GHz || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust amplitude at 857 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits|link=COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits}} (version 2.01 includes the error map)<br>{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.00.fits}}|| 2048 || NA || 353 GHz || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust optical depth at 353 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits|link=COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits}} (version 2.01 includes the error map)<br>{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.00.fits}} || 2048 || NA || NA || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust emissivity index<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits|link=COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits}} (version 2.01 includes the error map)<br>{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.00.fits}} || 2048 || K || NA || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust temperature<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Radiance_2048_R2.00.fits|link=COM_CompMap_Dust-GNILC-Radiance_2048_R2.00.fits}} || 2048 || W/m<sup>2</sup>/sr || NA || {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || Thermal dust radiance<br />
|-<br />
| {{PLASingleFile|fileType=map|name=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits|link=COM_CompMap_Dust-GNILC-Beam-FWHM_0128_R2.00.fits}} || 128 || Arcminute || NA || NA || Effective dust beam FWHM<br />
|-<br />
|}<br />
<br />
====CIB maps====<br />
<br />
<br />
{| border="1" cellpadding="5" cellspacing="0" align="center" style="text-align:center"<br />
|+ HDU -- COMP-MAP-CIB<br />
|-<br />
|- bgcolor="ffdead" <br />
! File Name || Nside || Units || Reference frequency || Angular resolution || Description<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_CIB-GNILC-F353_2048_R2.00.fits|link=COM_CompMap_CIB-GNILC-F353_2048_R2.00.fits}} || 2048 || MJy/sr || 353 GHz || 5 arcmin || CIB amplitude at 353 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_CIB-GNILC-F545_2048_R2.00.fits|link=COM_CompMap_CIB-GNILC-F545_2048_R2.00.fits}} || 2048 || MJy/sr || 545 GHz || 5 arcmin || CIB amplitude at 545 GHz<br />
|-<br />
|{{PLASingleFile|fileType=map|name=COM_CompMap_CIB-GNILC-F857_2048_R2.00.fits|link=COM_CompMap_CIB-GNILC-F857_2048_R2.00.fits}} || 2048 || MJy/sr || 857 GHz || 5 arcmin || CIB amplitude at 857 GHz<br />
|-<br />
|}<br />
<br />
== References ==<br />
<References /><br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Category:Mission products|007]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11653MediaWiki:Sidebar2018-08-07T14:15:24Z<p>Mlopezca: </p>
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** mainpage | WARNING: The 2015 Explanatory Supplement has moved to another location.</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11652MediaWiki:Sidebar2018-08-07T14:14:35Z<p>Mlopezca: </p>
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** mainpage | '''WARNING: The 2015 Explanatory Supplement has moved to another location.'''</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11651MediaWiki:Sidebar2018-08-06T21:29:51Z<p>Mlopezca: </p>
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** mainpage | WARNING: The 2015 Explanatory Supplement has moved to another location.</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11650MediaWiki:Sidebar2018-08-06T21:28:47Z<p>Mlopezca: </p>
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** mainpage | WARNING: 2015 Explanatory Supplement Discontinued</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11649MediaWiki:Sidebar2018-08-06T21:22:04Z<p>Mlopezca: </p>
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** mainpage | WARNING: 2015 ES Discontinued</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11648Main Page2018-08-06T21:21:12Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:130%"> The latest Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015), including this one, are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11647Main Page2018-08-06T21:21:02Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:130%"> The latest Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015), including this one, are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11646Main Page2018-08-06T21:20:50Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<span style="color:#ff0000" "font-size:130%"> The latest Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015), including this one, are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11645Main Page2018-08-06T21:20:22Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:130%"> The latest Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015), including this one, are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11644MediaWiki:Sidebar2018-08-06T21:19:48Z<p>Mlopezca: </p>
<hr />
<div>* navigation<br />
** mainpage| WARNING</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11643MediaWiki:Sidebar2018-08-06T21:18:38Z<p>Mlopezca: </p>
<hr />
<div>* navigation<br />
** mainpage| Main Index<br />
* WARNING<br />
** 2015 ES discontinued | WARNING page</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11642MediaWiki:Sidebar2018-08-06T21:17:32Z<p>Mlopezca: </p>
<hr />
<div>* navigation<br />
** mainpage| Main Index<br />
* WARNING<br />
** WARNING page | WARNING page</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11641MediaWiki:Sidebar2018-08-06T21:14:23Z<p>Mlopezca: </p>
<hr />
<div>* navigation<br />
** mainpage| Main Index<br />
* <span style="color:#ff0000" "font-size:120%"> WARNING </span></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=MediaWiki:Sidebar&diff=11640MediaWiki:Sidebar2018-08-06T21:12:20Z<p>Mlopezca: </p>
<hr />
<div>* navigation<br />
** mainpage| Main Index<br />
* Warning</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11639Main Page2018-08-06T09:22:02Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:130%"> The latest Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015), including this one, are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11638Main Page2018-08-06T08:06:47Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:130%"> The Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015) are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11637Main Page2018-08-06T08:06:36Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000" "font-size:115%"> The Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015) are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11636Main Page2018-08-06T08:04:34Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
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* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
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* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<span style="color:#ff0000"> The Planck Legacy Explanatory Supplement (https://wiki.cosmos.esa.int/planck-legacy-archive) contains the descriptions of all the Planck products, including the 2013, 2015 and 2018 data releases. Previous instances of the Explanatory Supplement (2013 and 2015) are not fully up-to-date and will be discontinued during 2018. </span> <br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11635Planck Added Value Tools2018-03-16T16:55:21Z<p>Mlopezca: /* Noise Map Cutout */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11634Planck Added Value Tools2018-03-16T16:52:17Z<p>Mlopezca: /* Map Making from Time-ordered data */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11633Planck Added Value Tools2018-03-16T16:51:52Z<p>Mlopezca: /* Custom Bandpasses */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11632Planck Added Value Tools2018-03-16T16:49:24Z<p>Mlopezca: /* Bandpass Leakage Correction */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11631Planck Added Value Tools2018-03-16T16:49:05Z<p>Mlopezca: /* Parametric model maximum likelihood component separation */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11630Planck Added Value Tools2018-03-16T16:48:47Z<p>Mlopezca: /* Masking */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11629Planck Added Value Tools2018-03-16T16:48:18Z<p>Mlopezca: /* Effective Beam Average */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11628Planck Added Value Tools2018-03-16T16:47:55Z<p>Mlopezca: /* Component Subtraction */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11627Planck Added Value Tools2018-03-16T16:45:39Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. <br />
# Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11626Planck Added Value Tools2018-03-01T15:17:47Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are Planck, WMAP and Herschel SPIRE.<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11625Planck Added Value Tools2018-03-01T15:16:38Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al in preparation.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11624Planck Added Value Tools2018-02-28T16:46:07Z<p>Mlopezca: /* Asynchronous Operations */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA. <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al. More documentation about this software can be found here.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
Title: Example of ring-based mapmaking choosing a four-month observing period.<br />
<br />
Describe the use of baseline offsets.<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11623Planck Added Value Tools2018-02-28T16:45:42Z<p>Mlopezca: /* Planck Sky Model */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA.<br />
*Planck Sky Model: Providing the user an interface to the PSM software, allowing users to create sky models using their own parameters. (Not yet public) <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al. More documentation about this software can be found here.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
Title: Example of ring-based mapmaking choosing a four-month observing period.<br />
<br />
Describe the use of baseline offsets.<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11622Planck Added Value Tools2018-02-28T13:20:00Z<p>Mlopezca: /* Asynchronous Operations */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
*Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
*Noise Map Cutout: Creating a cutout of the noise maps in the PLA.<br />
*Planck Sky Model: Providing the user an interface to the PSM software, allowing users to create sky models using their own parameters. (Not yet public) <br />
*Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
*Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Planck Sky Model==<br />
This functionality provides an interface to the Planck Sky Model (PSM), the software used internally in Planck to simulate sky maps and instrumental observations. More detailed documentation can be found here.<br />
Are you referring to the PSM Manual ? Fine. But I think that we need to include a short summary for the user here. (after all, the reason for making this interface is to make it easy for the user). The summary should be such that if the user wants to run a "simple" case (defaults values with small variations), he should not need to look up the manual. The manual should be referred to for "complex" cases.<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al. More documentation about this software can be found here.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
Title: Example of ring-based mapmaking choosing a four-month observing period.<br />
<br />
Describe the use of baseline offsets.<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11621Planck Added Value Tools2018-02-28T13:04:45Z<p>Mlopezca: /* Custom Bandpasses */</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
Noise Map Cutout: Creating a cutout of the noise maps in the PLA.<br />
Planck Sky Model: Providing the user an interface to the PSM software, allowing users to create sky models using their own parameters.<br />
Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Planck Sky Model==<br />
This functionality provides an interface to the Planck Sky Model (PSM), the software used internally in Planck to simulate sky maps and instrumental observations. More detailed documentation can be found here.<br />
Are you referring to the PSM Manual ? Fine. But I think that we need to include a short summary for the user here. (after all, the reason for making this interface is to make it easy for the user). The summary should be such that if the user wants to run a "simple" case (defaults values with small variations), he should not need to look up the manual. The manual should be referred to for "complex" cases.<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al. More documentation about this software can be found here.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
Title: Example of ring-based mapmaking choosing a four-month observing period.<br />
<br />
Describe the use of baseline offsets.<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by the Commander team from their 32-band run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planck_Added_Value_Tools&diff=11620Planck Added Value Tools2018-02-28T12:59:00Z<p>Mlopezca: Created page with "{{DISPLAYTITLE:Planck Added Value Tools Documentation}} ==General information about the functionality offered== The PLA offers several functionalities for the user to manipul..."</p>
<hr />
<div>{{DISPLAYTITLE:Planck Added Value Tools Documentation}}<br />
<br />
==General information about the functionality offered==<br />
The PLA offers several functionalities for the user to manipulate the data products it contains. Some of this functionality is intended to allow users to modify the products themselves, while other parts is intended to allow users to use the existing products to create new data.<br />
<br />
The are broken down into two separate categories, based on their synchronous or asynchronous nature of their implementation and are summarised here, but further explained in detail in the later parts of the Explanatory Supplement.<br />
<br />
===Synchronous Operations===<br />
The following list of operations, belonging in the first category, have the characteristic that the operations take place in realtime (or near-realtime) and that the user gets back their answer in a matter of seconds.<br />
<br />
These operations include:<br />
<br />
#Component subtraction: Allowing a user to subtract from a map certain physical components<br />
#Unit conversion: Allowing a user to convert the units of a map.<br />
#Colour correction: Allowing the user to convert a map to a different assumed spectral index. Bandpass transformation: Allowing the user to convert a map to what it would look like if observed with a different bandpass.<br />
#Masking: Allowing a user to mask away parts of a map, using existing or user-defined masks.<br />
#Bandpass Leakage Correction: Allowing the user to undo leakage corrections that were applied by the Planck team.<br />
<br />
All of the above functionalities are available both for map cutouts and for full sky maps. <br />
<br />
In the case of the Full Map Operations the responses are in an Asynchronous format (read next section) due to the size and the multiple number of maps selected.<br />
<br />
The user can select one or more of these functionalities to apply to one or more full maps, or a cutout. If several functionalities are selected, they are applied in the order above, i.e. component subtraction takes place first, then unit conversion, colour correction, bandpass transformation and finally masking. Note, however, that certain functionalities might invalidate others; for example, in order to perform colour correction, a map must be in (M)Jy/sr units, and if the user does not convert a map to this unit, colour correction will not take place.<br />
<br />
===Asynchronous Operations===<br />
In this category, the operations performed by the user will be queued by execution by the PLA server, and the results will be emailed back to user in the form of download links, at a later time when the execution has been completed. The user will then be able at their convenience download their results.<br />
<br />
In this category the following operations are included:<br />
<br />
Effective Beam Average: Allowing a user to calculate the average of the Planck effective beams over a given cutout of the map.<br />
Noise Map Cutout: Creating a cutout of the noise maps in the PLA.<br />
Planck Sky Model: Providing the user an interface to the PSM software, allowing users to create sky models using their own parameters.<br />
Map-Making: Providing the user with several tools to create a map cutout from the time-ordered data.<br />
Component Separation: Providing the user with the possibility to use Planck data to produce maps of various astrophysical components.<br />
<br />
<br />
==Component Subtraction==<br />
This functionality allows the user to subtract either a CMB map or parametric component map from a target frequency map. Details about the components to be subtracted can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/CMB_and_astrophysical_component_maps here]:<br />
Generally, the user will select a number of ‘components’, of which CMB maps can be a part, and the algorithm will use the spectral model of that component to extrapolate the component signal to the frequency of the target frequency map. Several components can be chosen, in which case what is subtracted from the target map is a sum of the extrapolated components. The subtraction takes place in harmonic space to avoid pixelization effects, and the user is allowed to select a resolution (‘Output NSIDE’) and smoothing FWHM with which to smooth both the components (‘Smoothing->Component’) before subtracting and the map (‘Smoothing->Target’) after subtraction. If the component map contains a polarization map as well, this can also be subtracted from the target map (if the target map contains polarized components, and if the components to be subtracted have a polarized model).<br />
Some of the components come in several resolutions; for these, the user will be allowed to choose (‘Available Nsides’). For these, the user is also informed about what the Nside of the map they have chosen is (‘Input Nside’). Note, however, that some of the resolutions might not contain a polarization component and selecting that resolution will disable the possibility to subtract the polarization component from the target map. <br />
<br />
'''CMB''': All CMB maps are available as temperature-only <math>N_{side}=2048</math> maps and temperature + polarization <math>N_{side}=1024</math> maps. In addition, the Commander temperature+polarization CMB map is available at <math>N_{side}=256</math>. These maps, before subtraction, are converted into the same unit as the target map using the appropriate bandpass (the one belonging to the target map)<br />
<br />
'''Other components''': The best-fit component maps from the 32-band run of [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html this paper] are available as <math>N_{side}=256</math> maps. Each component is extrapolated to the frequency of the target map using the rules for the spectral behaviour of those components, detailed in the abovementioned paper. They are then converted to the same unit as the target map before they are subtracted.<br />
<br />
These components consist of:<br />
<br />
“Synchrotron”: A low-frequency template (GALPROP z10LMPD_SUNfE from Orlando and Strong (2013)) with an amplitude and pivot frequency fit. The temperature best-fit parameters used for this component are contained in COM_CompMap_Synchrotron-commander_0256_R2.00.fits, while the polarization parameters are contained in COM_CompMap_SynchrotronPol-commander_0256_R2.00.fits.<br />
<br />
“Free-free”: The Draine (2011) two-parameter bremsstrahlung model, parametrized by the emission measure and electron temperature. The map containing the best-fit parameters is COM_CompMap_freefree-commander_0256_R2.00.fits.<br />
<br />
“Spinning dust”: Two templates derived from SpDust2 (Ali-Haïmoud et al., 2009, Ali-Haïmoud 2010, Silsbee et al., 2011), each with separate amplitudes, and the first template with a spatially varying peak frequency, while the second template has a spatially constant peak frequency. The parameter file containing the best-fit parameters in this case is COM_CompMap_AME-commander_0256_R2.00.fits.<br />
<br />
“Thermal dust”: Three-parameter (including the amplitude) modified blackbody model, parametrized by the emissivity index and dust temperature. There are three available best-fit parameter maps for this component: The Nside=256 map from the 32-band solution, COM_CompMap_dust-commander_0256_R2.00.fits, and two high-resolution parameter maps from a run using only the Planck data at 143 GHz and above: The temperature + polarization Nside 1024 map, COM_CompMap_DustPol-commander_1024_R2.00.fits, and the temperature-only Nside 2048 map, COM_CompMap_ThermalDust-commander_2048_R2.00.fits.<br />
<br />
“SZ”: The thermal Sunyaev-Zeldovich effect, parametrized by y_sz. The best-fit parameters of this component are contained in COM_CompMap_SZ-commander_0256_R2.00.fits.<br />
<br />
“Line emission”: The CO lines (3->2, 2->1, 1->0) and the 94/100 GHz line emission signals. These are only available for the frequencies at which they make a contribution. For CO1->0 this is the 100 GHz and sub-detectors, for CO2->1 this is 217 GHz and sub-detectors, and for CO3->2 this is 353 GHz and sub-detectors. The 94/100 GHz emission is also at the 100 GHz detectors. In the case of CO emission, the file containing the best-fit parameters is COM_CompMap_CO-commander_0256_R2.00.fits, while for 94/100 GHz, the file is COM_CompMap_xline-commander_0256_R2.00.fits.<br />
<br />
For some of the above components (free-free, thermal dust, and SZ) we give the user the possibility to replace the best-fit parameters from Commander by a single global value. <br />
<br />
Once the component maps are in the correct unit, they, along with the target map, are transformed into the harmonic domain. If there are more than one component map chosen, they will be added together, and then the sum will be subtracted from the target map. The user can then choose to smooth the final map before projecting onto the desired resolution.<br />
<br />
The output file will be of the same format as the input, except that it will have had the selected components subtracted from it, and will be of the resolution that the user chose.<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map, before component subtraction<br />
<br />
<br />
Title: The HFI 100 GHz full frequency map after subtracting the CMB component as estimated by SMICA.<br />
<br />
==Factor Computations==<br />
The unit conversion, colour correction, and bandpass transformation functionalities all have in common that they calculate a factor and multiply either a full map or map cutout by that factor. They also provide the option to calculate an error estimate of this factor, in the cases where the bandpass responses also have corresponding uncertainty values; only the HFI bandpasses has this, so for LFI bandpasses this functionality is disabled. The uncertainty is estimated by generating Monte Carlo samples, following the same procedure outlined [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here] These values, when this option is selected, are included in the header of the resulting map file.<br />
<br />
===Unit Conversion and Colour Correction===<br />
The unit conversion and colour correction functionalities are interfaces to the uc_cc code used internally in Planck, and whose documentation can be found [https://wiki.cosmos.esa.int/planckpla2015/index.php/Unit_conversion_and_Color_correction here].<br />
<br />
===Bandpass Transformation===<br />
The bandpass transformation functionality follows the same structure as the unit conversion and colour correction functionalities, though the goal here is slightly different. Defining the monochromatic flux density <math>\tilde{S}</math> as<br />
<br />
<math><br />
\int{R(\nu)S(\nu)d\nu}= \tilde{S}(\nu_0)\int{R(\nu)f(\nu)d\nu}<br />
</math><br />
<br />
where R is the bandpass response of the bandpass in question, S is the actual flux density of the source, and f is the spectral behaviour of the reference spectrum (e.g. <math>\nu/\nu_0</math> in the case of the IRAS convention), the goal of bandpass transformation is to find the monochromatic flux density for the same source and reference spectrum given a different bandpass response. Since the ratio of the left hand and right hand side of the equation above should be 1 with any bandpass, we have<br />
<br />
<math><br />
\tilde{S}_{new}(\nu_{0, new})=\frac{\int{R_{old}(\nu)f(\nu,\nu_{0, old})d\nu}}{\int{R_{new}(\nu)f(\nu,\nu_{0, new})d\nu}}\cdot\frac{\int{R_{new}(\nu)S(\nu)d\nu}}{\int{R_{old}(\nu)S(\nu)d\nu}}\tilde{S}_{old}(\nu_{0, old}).<br />
</math><br />
<br />
This equation forms the basis of the bandpass transformation algorithm. In order to use the functionality, it is therefore necessary to specify both the reference spectrum and the assumed spectral behaviour of the source. The reference spectra are either ‘thermodynamic’, ‘iras’, ‘ysz’, or ‘brightness’ (Rayleigh-Jeans or antenna temperature). The assumed spectral behaviours are either ‘powerlaw’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \bigl(\frac{\nu}{\nu_0}\bigr) ^ \alpha<br />
</math><br />
<br />
or ‘modified blackbody’, which is a spectrum obeying this behaviour (in brightness units):<br />
<br />
<math><br />
S(\nu) = \frac{2\hbar\nu^3}{c^2 \exp(x(\nu, T)) - 1} \cdot \nu ^ \beta<br />
</math><br />
<br />
where <math>x(\nu, T) = \frac{\hbar \nu}{k_bT}</math>.<br />
<br />
Uncertainty estimation is performed in the same way as in uc_cc: given uncertainty values of the bandpass response, we can generate Monte Carlo samples by repeatedly simulating bandpass responses and calculating the bandpass transformation factor for those responses. The standard deviation of these samples is then reported as the uncertainty of the factor.<br />
<br />
===Masking===<br />
This functionality gives the user the possibility to mask away areas of a sky map using either a PLA mask, a mask generated from source catalogues, or a custom uploaded mask. The basic masking operation is straightforward: For every masked pixel, that pixel in the target map is set to zero.<br />
<br />
The first option is using a predefined mask stored in PLA, all of which can be found using the dropdown tool. Each mask file can contain several columns, each of which typically represents various threshold values chosen for the masks.<br />
<br />
The second option is generating a mask from one or more source catalogues. The user will then choose the source catalogues to use.<br />
<br />
The user then has to select the radius around each source they want to mask away. There are two different ways to define this radius: Either a fixed radius, defined by a value and a unit, or a dynamic radius that depends on the signal-to-noise ratio of the source measurement. This is the algorithm that was used in the generation of the LFI masks. The exact equation that is used is<br />
<br />
<math><br />
r = \frac{FWHM}{2 \sqrt{2\log(2)}}\cdot f<br />
</math><br />
<br />
where<br />
<br />
<math><br />
f = \sqrt{2 \log\bigl(\frac{amp}{0.1 * noise}\bigr)}<br />
</math><br />
<br />
Here, amp and noise are columns from the source catalogue and describe some flux measure and uncertainty measure connected to that flux, respectively. The user also defines the FWHM in the above equation.<br />
<br />
After selecting the radius around each source, the user can define filters which take away sources that do not meet certain criteria. There are three types of filters: A) Those that filter away those sources where '''a given column''' is greater/lower than a given value, B) those that filter away those sources where '''the ratio of two given columns''' is greater/lower than a given value, and C) those that filter away those sources where '''a flag is set to False'''.<br />
<br />
Several such filters can be created, and each of them is applied to a given source catalogue.<br />
<br />
The third option is uploading a custom mask. This mask must be a FITS file containing a single extension (excepting the primary extension), and the first column of this file will be used. The mask must be in HEALPix format, and if the NSIDE and/or ORDERING parameters of this mask is different than the target map, the mask will be automatically converted before application (when downgrading the mask, we perform a simple average of the subpixels to determine whether the superpixel should be masked; if its value is >= 0.5, it will be). The FITS <br />
file can be zipped as a tar.gz file.<br />
<br />
The output file will contain the input map, with the pixels indicated by the chosen mask set to zero. The mask itself will also be appended as a separate column at the end of the FITS extension.<br />
<br />
Title: The LFI 44 GHz full sky map with a dynamic radius mask from the 44 GHz source catalogue.<br />
<br />
==Noise Map Cutout==<br />
The purpose of this functionality is to generate a noise map cutout from one of the simulated noise realizations available in the PLA using the same cutting settings (region of interest, resolution, and rotation angle) used to generate the associated frequency map cutout, at that frequency and for the same time coverage.�<br />
<br />
==Effective Beam Average==<br />
The purpose of this functionality is to provide the user with with an average over the effective beams for a map cutout. Given a cutout, the algorithm takes the beam whose center pixel is the four corners of the cutout, as well as the central pixel of the cutout, and it then performs a weighted average of these five beams, where the weights are given by the number of hits in the center pixel of the beam. The effective beams are defined in [https://wiki.cosmos.esa.int/planckpla2015/index.php/Effective_Beams this document].<br />
<br />
Title: The average of the effective 545 GHz beams in the Crab nebula region.<br />
<br />
<br />
==Planck Sky Model==<br />
This functionality provides an interface to the Planck Sky Model (PSM), the software used internally in Planck to simulate sky maps and instrumental observations. More detailed documentation can be found here.<br />
Are you referring to the PSM Manual ? Fine. But I think that we need to include a short summary for the user here. (after all, the reason for making this interface is to make it easy for the user). The summary should be such that if the user wants to run a "simple" case (defaults values with small variations), he should not need to look up the manual. The manual should be referred to for "complex" cases.<br />
<br />
==Map Making from Time-ordered data==<br />
The majority of the maps ingested in the PLA are those generated by the Planck Data Processing Centres (DPCs). In addition to maps, the PLA also provides access to Time-ordered data, which can be used to generate new maps. The new maps will be different from those in the PLA since the map-making method is different, and the selection of Time-ordered data may be different. We caution the user that the map-making methods used by PLA are much less sophisticated than the ones used in the Planck DPCs. The new maps generated should only be used as a rough initial approximation, for example to gauge the effect of deselecting part of the input time-ordered data. <br />
<br />
Two map making tools are provided by PLA: the first, ring-based map making, provides an interface to the rings-based map making software written by Keihanen et al. More documentation about this software can be found here.<br />
<br />
The option ‘Remove temperature monopole’ subtracts the average value of the map pixels from all pixels.<br />
<br />
The output from the ring-based map making is three FITS files each containing one or more full-sky HEALPix maps: The map itself (I or IQU), the hit map (number of observations in each pixel) and the white noise covariance matrix (either 1 column if only temperature is selected, or six columns containing all IQU correlations if polarization is selected as well).<br />
<br />
<br />
Title: Example of ring-based mapmaking choosing a four-month observing period.<br />
<br />
Describe the use of baseline offsets.<br />
<br />
The second map-making option, called ‘Baseline-removed pixel averaging ’, is a timeline based mapmaker. It basically consists of binning individual samples into Healpix pixels, and is therefore the simplest possible map-making. The user selects a region or timespan, along with instrumental parameters, and the timelines corresponding to the parameters chosen are processed in the following way (this assumes both temperature and polarization maps are requested; the temperature-only case is analogous, but simpler):<br />
<br />
We first define <math>s_r</math> and <math>s^2_r</math> as the baseline-subtracted signal observed at sample <math>r</math>, and the signal observed at sample <math>r</math> squared, respectively (to be clear: <math>s^2 \doteq (s_1^2, s_2^2\dots)</math> etc., and not the alternative interpretation where <math>s^2 = s^Ts</math>. Alternatively, one could say that <math>s^2 = diag(s^Ts)</math>). These will be nsample-sized vectors.<br />
<br />
In order to baseline-subtract the signal, we follow two different approaches for LFI and HFI, respectively:<br />
<br />
LFI: In this case the subtraction is simple: Each signal timeline comes with a corresponding baseline, and we simply subtract the offset for the given detector from the signal for that detector.<br />
<br />
HFI: In this case the offset data for all timelines is stored in a single file, and we must use the metadata of the signal timeline to find the offset array that corresponds to that timeline. The way this is done is by first extracting the 'BEGRING', 'ENDRING', 'BEGIDX' and 'ENDIDX' keywords from the signal timeline, and comparing these with the data in HFI_ROI_DestripeOffsets_R2.02_full.fits and HFI_ROI_GlobalParams_R2.02_full.fits in the following way:<br />
<br />
First the 'start ring index' is found by subtracting the 'BEGRING' of the global parameter file from the 'BEGRING' of the science timeline.<br />
The 'end ring index' is similarly found by subtracting the 'BEGRING' of the global parameter file from the 'ENDRING' of the science timeline.<br />
<br />
For each ring index between the start and end ring indices, we locate the sample indices inside the given ring by fetching all sample indices between ring index and ring index +1. This is done by accessing the 'BEGIDX' field of the global parameter data from ring index and ring index + 1. We then map this set of sample indices to the actual offset data for that ring, which we do by going into the DestripeOffsets file and access the offsets at the given indices (belonging to that specific ring). We do this by accessing the ring index'th row and of the DestripeOffsets data, also taking care to use the right detector name to access the correct field. We use the first element of the resulting array, which corresponds to the full map offset (as opposed to first and second half-ring, which is the two other elements of that array).<br />
<br />
We now then have the offset data for all rings between BEGRING and ENDRING (in the signal timeline) mapped to a sample index. We can then access the offset data corresponding to the sample indices we actually need, which are the sample indices between BEGIDX and ENDIDX from the signal timeline. From these we create the final offset array and subtract it from the signal timeline.<br />
<br />
We then define the pointing matrix <math>A</math> as the <math>N_{pix} \times N_{samples}</math> matrix where the entry corresponding to pixel <math>p</math> and sample <math>r</math> is given by<br />
<br />
<math><br />
A_{p, r} = i(p, r) \cdot [1, \cos(2 \psi(r)), \sin(2 \psi(r))],<br />
</math><br />
<br />
Where <math>i</math> is a function that is 1 if the sample falls within the pixel <math>p</math> and 0 otherwise, and <math>\psi</math> is the pointing angle of sample <math>r</math>. This means that inside each element of <math>A</math> is embedded a 3-dimensional space containing pointing information for that element.<br />
<br />
We then create the diagonal <math>N_{pix} \times N_{pix}</math> matrix <math>W</math>, defined as<br />
<br />
<math><br />
W = A^TA,<br />
</math><br />
<br />
Where now each diagonal element of <math>W</math> is a <math>3\times 3</math> matrix.<br />
Each element of <math>W</math> thus looks like this (We let <math> T</math> be the set of all timelines, and <math>ns</math> is the function giving the number of samples in a timeline):<br />
<br />
<math><br />
W_{p, p} = \sum_{q\epsilon T} \sum_{r=1}^{ns(q)} i(p, r) \cdot<br />
\begin{pmatrix}<br />
1 & \cos(2 \psi(r)) & \sin(2 \psi(r)) \\<br />
\cos(2 \psi(r)) & \cos^2(2\psi(r)) & \cos(2 \psi(r))\sin(2\psi(r)) \\<br />
\sin(2 \psi(r)) & \cos(2\psi(r)\sin(2\psi(r)) & \sin^2(2\psi(r)))<br />
\end{pmatrix}<br />
</math><br />
<br />
<br />
<br />
The map, <math>m</math>, in pixel <math>p</math> will then be given by<br />
<br />
<math><br />
m_p = (A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts)_{p, p},<br />
</math><br />
<br />
where all the matrix operations now happen in the sub-space mentioned above, except <math>A^Ts</math> which projects the <math>n_{samp}</math>-sized vector onto the <math>n_{pix}</math>-sized space.<br />
<br />
Further, the rms of pixel <math>p</math> is given by<br />
<br />
<math><br />
rms_p = \frac{1}{n_{samp}}\sqrt{(diag((A_{p, p}^TA_{p, p})^{-1} \cdot (A^Ts^2)_{p, p}) - m_p^Tm_p)},<br />
</math><br />
<br />
Where again, all matrix operations except for <math>A^Ts^2</math> happen in the sub-dimension, and <math>n_{samp}</math> is the number of samples binned into pixel <math>p</math>. This quantity is given by the <math>(1, 1)</math> element of <math>W</math>.<br />
<br />
For temperature only, the 3-dimensional sub-space is reduced to 1 dimension, and all operations in that space reduce to simple scalar operations - i.e. weighted averaging of all the timeline samples falling in each pixel.<br />
<br />
The output from this mapmaker is <math>m, rms</math>, and <math>W</math>.<br />
<br />
<br />
<br />
<br />
Explain the options: Planets timelines; use ringhalf offsets.<br />
<br />
==Component Separation==<br />
Component separation is the process of estimating the contribution of a specific source of emission to an observed map. The PLA provides a number of all-sky maps of components which have been derived from Planck observations by the Planck Data Processing Centres. These maps have been generated using specific algorithms optimized in certain ways, using certain input data, and targeted to produce all-sky maps. The PLA allows to generate new estimates of components using Planck observations. Both are very simplified versions of the DPC algorithms, but may offer certain advantages as they allow to select the input data, and to target small regions of the sky.<br />
<br />
The PLA offers two methods to carry out component separation: an Internal Linear Combination algorithm (ILC), and a parametric model maximum likelihood algorithm. The ILC method allows to estimate the CMB only, whereas the parametric method allows to estimate a set of sources defined by model SEDs. Both can be applied either to the entire sky or to a specified cutout. <br />
<br />
<br />
===ILC component separation===<br />
<br />
The ILC component separation algorithm follows the standard ILC procedure (e.g. Bennett, C. L. et al. 2003b, ApJS, 148, 97): Assume that for a map observed at a given channel <math>k</math> we have<br />
<br />
<math><br />
T(\nu_k) = T_{cmb} + T_{res}(\nu_k),<br />
</math><br />
<br />
Where <math>T_{cmb}</math> is the CMB contribution and is assumed to be independent of channel as long as our map is in thermodynamic units. We then form the combination<br />
<br />
<math><br />
T = \sum_k w_kT(\nu_k)<br />
</math><br />
<br />
Where <math>w_k</math> is some weight applied to that particular channel, which is under the constraint that<br />
<br />
<math><br />
\sum_k w_k = 1.<br />
</math><br />
<br />
With this constraint,<br />
<br />
<math><br />
T = T_{cmb} + \sum_k w_k \cdot T_{res}(\nu_k),<br />
</math><br />
<br />
meaning that if we can choose <math>w_k</math> that will minimize the second sum, we end up with a map that is close to the CMB contribution. The weights that will minimize this sum is given by<br />
<br />
<math><br />
w_k = \frac{\sum_j C_{k, j}^{-1}}{\sum_{i, j} C_{i, j}^{-1}}<br />
</math><br />
Where <math>C</math> is the <math>n_{maps} \times n_{maps}</math> sample covariance matrix.<br />
<br />
<br />
If polarization is enabled, there will be two sets of weights: one for the <math>I</math> field, and one for the <math>Q</math> and <math>U</math> fields. Only those selected maps that have a polarization component will be used for calculating the <math>Q</math> and <math>U</math> weights.<br />
<br />
The user can select a number of Planck maps and a selection of external maps, as well as a target resolution and degree of smoothing. The function will convert the map to thermodynamic temperature and smooth all maps in harmonic space with the target smoothing FWHM before projecting it onto a map with the target <math>N_{side}</math>. This is done regardless if the maps have different resolutions, and it is recommended to choose an FWHM that corresponds to the lowest-resolution map (or lower). The system does not attempt to sanity check the FWHM that the user prescribes.<br />
<br />
The output from the ILC functionality is a file containing, as its first 1, 2, or 3 columns (depending on whether I, QU or IQU was chosen, respectively), the ILC solution. The subsequent columns contains the residuals of each of the input maps, defined as the ILC solution subtracted from the input map at each channel (after the input maps have been converted to K_CMB). The number of residual columns is equal to n * (1, 2, or 3), where n is the number of input maps and the last factor again depends on whether I, QU or IQU was chosen.<br />
<br />
<br />
===Parametric model maximum likelihood component separation===<br />
<br />
This method uses the 32-band temperature data and parametric model described in the [https://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html 2015 diffuse components paper]. The user can select which of the 32 bands and which of the parametric components they wish to include in the analysis. They also must choose a region in which to perform the analysis.<br />
<br />
Using this data we can define a chi-squared for each pixel as follows:<br />
<br />
<math><br />
\chi^2 = \sum_{i=1}^{n_{bands}} \frac{(m(i) - d_i) ^ 2}{rms_i^2},<br />
</math><br />
where <math>m(i)</math> is the model evaluated at band <math>i</math> and <math>d_i</math> and <math>rms_i</math> is the signal observed by, and the rms corresponding to that band, respectively.<br />
<br />
We start from the parameter results from the abovementioned paper and run a Powell minimization algorithm to minimize the chi-squared until we have found parameter values for all pixels in the target region.<br />
<br />
Global parameters are fixed to their best-fit values from the 2015 paper, as are monopole and dipole values.<br />
<br />
Output from this functionality are the best-fit parameter values of the components that the user has chosen to include, as well as the chi-squared value in each pixel. The user can also elect to get as output the residual maps in a single file, meaning the data minus the best-fit model. As the data are given in <math>N_{side}=256</math> and smoothed to 60 arcmin, the output residual maps and parameters will also have this resolution, although they will be reprojected onto the cutout the user has chosen.<br />
<br />
Note that results from this will likely be poorer in regions of high activity where we have a limited understanding of the physical processes, such as close to the galactic center. See the masks and maps in the abovementioned paper to get an idea of which regions might be less contaminated and thus give better results.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust amplitude.<br />
<br />
Title: Example output from the parametric model maximum likelihood component separation on a cutout: The thermal dust spectral index.<br />
<br />
<br />
==Bandpass Leakage Correction==<br />
<br />
The purpose of this functionality is to add or subtract correction templates available in the PLA from the associated frequency maps to “apply” or “remove” corrections that have been introduced by the Planck Data Processing Center to correct mainly for systematic effects. It is up to the advance users of the PLA to use this functionality, but the Planck Collaboration recommends not to use uncorrected maps for science analysis unless the user understands fully the impact of removing these corrections. By selecting the correction map to be applied or removed based on the type of correction, frequency and mission coverage, the system will automatically know to which map to apply the correction, or from which map to remove the correction, and as a result, the uncorrected map will be generated.<br />
<br />
My recollection is that the AVI should be able to both "Do" and "Undo" corrections ?<br />
<br />
Explain the "perform cutting" option.<br />
<br />
Describe the output files.<br />
<br />
<br />
==Custom Bandpasses==<br />
There are several functionalities that allow the user to upload custom bandpasses: Unit conversion, colour correction, bandpass transformation, ILC component separation, and component subtraction. These bandpasses must be an ASCII file containing two or three columns. The first column contains the frequency at which the response is defined, given in Hz. The second contains the actual bandpass response, and the optional third column contains the uncertainty of the bandpass response, assumed to be one standard deviation. If this column is present, an uncertainty estimate will be provided (for those functionalities that provide such an estimate).<br />
<br />
<br />
The currently available bandpasses are<br />
<br />
The SPIRE PLW and PMW bandpasses, based on the data found [ftp://ftp.sciops.esa.int/pub/hsc-calibration/latest_cal_tree/spire_cal_14_3.jar here]. Note that the bandpasses we currently possess are created using lambda-squared weighting, which the SPIRE team has since changed, so these do not correspond to the current state of the SPIRE bandpasses.<br />
The WMAP bandpasses, as delivered by Ingunn Wehus and Hans Kristian Eriksen from their 32-band Commander run for the 2015 diffuse components paper.<br />
<br />
<br />
A custom bandpass should be:<br />
*A text file<br />
*Comment lines should be started with a # character<br />
*Comments are allowed only in the top of the file<br />
*There is no need for column headers<br />
*The columns can be space or tab-separated<br />
*There is no need for the columns to be aligned vertically<br />
*The structure is a 3 column file with the following columns: frequency, response and standard_error<br />
*To prevent abuse of the system, there can be a total of 50,000 lines in the file<br />
<br />
<br />
<br />
==References==<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11619Main Page2018-02-28T12:46:17Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
##[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11618Main Page2018-02-28T12:45:59Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]]<br />
###[[Planck Added Value Tools | Planck Added Value Tools]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Detector_pointing&diff=11617Detector pointing2018-01-12T13:20:41Z<p>Mlopezca: /* Beam Rotation */</p>
<hr />
<div>== Introduction and Summary ==<br />
<br />
The overall geometry of the Planck focal plane is shown here:<br />
<br />
[[Image:FocalPlane.png| thumb|500px|center| The Planck Focal Plane]]<br />
<br />
In order to take full advantage of the Planck beams, we must know the individual detector pointing positions to a precision of better than several arcseconds, over the course of the entire mission. <br />
<br />
Spacecraft pointing comes from the on-board star tracker sampled at 8 Hz between repointings (Attitude History File, AHF). This is translated via a series of three-dimensional rotations to a pointing for the centre of the focal plane and resampled to the HFI or LFI TOI data rate for convenience. We must then further rotate this focal-plane boresight pointing to the individual detector locations. Because neither the rotations from the star tracker to the boresight nor those from the boresight to the individual detectors are known exactly a priori, we must calibrate them using flight data.<br />
<br />
Specifically, measurements of HFI and LFI detector pointing are based largely on observations of the brighter planets, with information from the much more frequent observations of lower-flux Galactic and extragalactic high-frequency sources used to monitor and build a model of overall pointing drift. This long-term drift of the spacecraft attitude is due to changes in the moment of inertia of the spacecraft, and also includes specific events which may induce sudden changes, essentially random as far as our ability to predict their effects is concerned. In this delivery HFI used a model (described below) to follow the pointing drift continuously, while LFI uses two different focal plane descriptions for the two time periods separated by operations perfomed on the instrument that modified the thermal behaviour. The two approach are consistent to better than a few arcsec. <br />
<br />
Note that for HFI the resulting pointing model cannot easily be directly compared to a physical/optical model: in particular, it includes a phase shift in the scan direction from the convolution and deconvolution of the detector transfer function, which is complex in the Fourier domain (see {{PlanckPapers|planck2013-p03c}}). This phase shift was not measured during normal operations, but a short campaign during which the spacecraft was spun at a higher rate will be used to determine these offsets in future date releases. Comparison with the initial optical model indicates that the in-scan change due to this phase shift is of the order of 1 arcminute. Note also that aberration is corrected in all observations.<br />
<br />
The final pointing model is measured to be better than 2 arcsecond rms in the co-scan and cross-scan directions averaged over ten-day periods, as shown below. Note that there are larger hourly drifts of up to 10 arcseconds due to interference from the radiometer electronics box assembly (REBA) as discussed more fully in {{PlanckPapers|planck2013-p03}}.<br />
<br />
==Stellar Aberration==<br />
<br />
The corrected quaternions are interpolated using Spherical Linear Interpolation algorithm and transformed in cartesian vector, which we call <math> DPT </math>. For each sample the stellar aberration correction is applied:<br />
<br />
:<math> DPT = DPT - {v_{sat} \over c } </math><br />
<br />
where <math> v_{sat} </math> is the satellite velocity and <math> c </math> is the speed of light. After this operation the vector is normalized.<br />
<br />
Finally the cartesian vetor is converted in Ecliptic Coordinates, the detector pointing.<br />
<br />
==Beam Rotation==<br />
<br />
The rotation of the beam with respect to North is the <math> \psi </math> angle and is computed rotating the corrected quaternions <math> Q </math>.<br />
<br />
The resulting rotation matrix represents the rotation of the beam, the <math> \psi </math> angle is then:<br />
<br />
:<math> \psi = -\arctan (R[0][1],R[0][0]) </math><br />
<br />
== Focal plane drift ==<br />
<br />
The low frequency pointing correction, known as PTCOR has been reworked to include satellite "wobble" angle corrections (difference between the satellite spin frame and the rigid body reference frame). The move was necessitated by problems in the measured wobble angles beyond the HFI mission (beyond survey 5). We also observed that the long time scale features in the pointing correction were very well fitted by a template constructed from the solar distance. This template was further augmented by including a linear fit component and breaks at times of known thermal disturbance onboard the spacecraft.<br />
<br />
[[Image:pointing_offset_070--143GHz_dx11_planets_only_CPP_1_v2.png | thumb|500px|center| Measured cross-scan offsets after applying the low frequency pointing correction (shown in orange).]]<br />
<br />
[[Image:pointing_offset_070--143GHz_dx11_planets_only_CPP_2_v2.png | thumb|500px|center| Measured in-scan offsets after applying the low frequency pointing correction (shown in orange).]]<br />
<br />
<br />
== References ==<br />
<References /><br />
<br />
<br />
<br />
<br />
[[Category:HFI/LFI joint data processing|001]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planets_related_data&diff=11616Planets related data2017-12-04T20:11:45Z<p>Mlopezca: </p>
<hr />
<div>The planet flux density measurements are described in a paper that is available in {{PlanckPapers|planck2017-LII}}.<br />
<br />
The raw data can be downloaded [[:File:pla_v3.zip|here]]. The interpretation of these data should become apparent from reading the paper. However, the following list aims to provide accurate descriptions of each of the parameters that we provide. <br />
<br />
The results are contained in 21 data files, one file for each planet observation made by Planck HFI. Each file contains 15 data columns. These data should allow the reader to reconstruct the Planck-HFI estimates of planet spectral radiance as well as associated uncertainties, both statistical and systematic.<br />
<br />
The columns are:<br />
* '''Channel''': The Planck-HFI detector name.<br />
* '''obs tims''': The observation time [MJD] corresponding to the time when this detector is centred on the planet.<br />
* '''RA''': Astrometric right ascension (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''DEC''': Astrometric declination (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''pda''': Solid angle [arcsec^2] of the planet used in the brightness analysis. <BR>This corresponds to $\Omega _\mathrm{p}$ in the paper (see discussion in Section 2.1). <br />
* '''dpda''': Assumed error in planet solid angle [arcsec^2].<br />
* '''T''': Planet thermodynamic temperature [K] (see definition in paper) at the band reference frequency <BR>corresponding to 100, 143, 217, 353, 545, or 857 GHz (see discussion in Section 2.1).<br />
* '''dT1''': Statistical uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''dT2''': Systematic uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''flux''': Flux density [Jy] estimate at the reference frequency found by calculating the product of the planet <BR>solid angle and the Planck blackbody formula with the planet thermodynamic temperature and reference frequency as inputs.<br />
* '''dflux1''': Statistical uncertainty in flux density [Jy].<br />
* '''dflux2''': Systematic uncertainty in flux density [Jy].<br />
* '''beam''': Scanning beam solid angle [arcmin^2] used for this channel. <BR>This corresponds to $\Omega _\mathrm{b}$ in the paper (see discussion in Section 2.1).<br />
* '''kappa1''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _1$ in the paper (see definition in Section 2.1).<br />
* '''kappa2''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _2$ in the paper (see definition in Section 2.1).<br />
<br />
<br />
== References ==<br />
<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planets_related_data&diff=11615Planets related data2017-12-04T20:06:06Z<p>Mlopezca: </p>
<hr />
<div>The planet flux density measurements are described in a paper that is available here {{PlanckPapers|planck2017-LII}}.<br />
<br />
The raw data can be downloaded [[:File:pla_v3.zip|here]]. The interpretation of these data should become apparent from reading the paper. However, the following list aims to provide accurate descriptions of each of the parameters that we provide. For any questions, please contact the corresponding author, Jon E. Gudmundsson: jon.gudmundsson@fysik.su.se / jegudmunds@gmail.com<br />
<br />
The results are contained in 21 data files, one file for each planet observation made by Planck HFI. Each file contains 15 data columns. These data should allow the reader to reconstruct the Planck-HFI estimates of planet spectral radiance as well as associated uncertainties, both statistical and systematic.<br />
<br />
The columns are:<br />
* '''Channel''': The Planck-HFI detector name.<br />
* '''obs tims''': The observation time [MJD] corresponding to the time when this detector is centred on the planet.<br />
* '''RA''': Astrometric right ascension (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''DEC''': Astrometric declination (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''pda''': Solid angle [arcsec^2] of the planet used in the brightness analysis. <BR>This corresponds to $\Omega _\mathrm{p}$ in the paper (see discussion in Section 2.1). <br />
* '''dpda''': Assumed error in planet solid angle [arcsec^2].<br />
* '''T''': Planet thermodynamic temperature [K] (see definition in paper) at the band reference frequency <BR>corresponding to 100, 143, 217, 353, 545, or 857 GHz (see discussion in Section 2.1).<br />
* '''dT1''': Statistical uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''dT2''': Systematic uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''flux''': Flux density [Jy] estimate at the reference frequency found by calculating the product of the planet <BR>solid angle and the Planck blackbody formula with the planet thermodynamic temperature and reference frequency as inputs.<br />
* '''dflux1''': Statistical uncertainty in flux density [Jy].<br />
* '''dflux2''': Systematic uncertainty in flux density [Jy].<br />
* '''beam''': Scanning beam solid angle [arcmin^2] used for this channel. <BR>This corresponds to $\Omega _\mathrm{b}$ in the paper (see discussion in Section 2.1).<br />
* '''kappa1''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _1$ in the paper (see definition in Section 2.1).<br />
* '''kappa2''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _2$ in the paper (see definition in Section 2.1).<br />
<br />
<br />
== References ==<br />
<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planets_related_data&diff=11614Planets related data2017-12-04T20:05:22Z<p>Mlopezca: </p>
<hr />
<div>The planet flux density measurements are described in a paper that is available on the arXiv: https://arxiv.org/abs/1612.07151 and published in Astronomy and Astrophysics {{PlanckPapers|planck2017-LII}}.<br />
<br />
The raw data can be downloaded [[:File:pla_v3.zip|here]]. The interpretation of these data should become apparent from reading the paper. However, the following list aims to provide accurate descriptions of each of the parameters that we provide. For any questions, please contact the corresponding author, Jon E. Gudmundsson: jon.gudmundsson@fysik.su.se / jegudmunds@gmail.com<br />
<br />
The results are contained in 21 data files, one file for each planet observation made by Planck HFI. Each file contains 15 data columns. These data should allow the reader to reconstruct the Planck-HFI estimates of planet spectral radiance as well as associated uncertainties, both statistical and systematic.<br />
<br />
The columns are:<br />
* '''Channel''': The Planck-HFI detector name.<br />
* '''obs tims''': The observation time [MJD] corresponding to the time when this detector is centred on the planet.<br />
* '''RA''': Astrometric right ascension (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''DEC''': Astrometric declination (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''pda''': Solid angle [arcsec^2] of the planet used in the brightness analysis. <BR>This corresponds to $\Omega _\mathrm{p}$ in the paper (see discussion in Section 2.1). <br />
* '''dpda''': Assumed error in planet solid angle [arcsec^2].<br />
* '''T''': Planet thermodynamic temperature [K] (see definition in paper) at the band reference frequency <BR>corresponding to 100, 143, 217, 353, 545, or 857 GHz (see discussion in Section 2.1).<br />
* '''dT1''': Statistical uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''dT2''': Systematic uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''flux''': Flux density [Jy] estimate at the reference frequency found by calculating the product of the planet <BR>solid angle and the Planck blackbody formula with the planet thermodynamic temperature and reference frequency as inputs.<br />
* '''dflux1''': Statistical uncertainty in flux density [Jy].<br />
* '''dflux2''': Systematic uncertainty in flux density [Jy].<br />
* '''beam''': Scanning beam solid angle [arcmin^2] used for this channel. <BR>This corresponds to $\Omega _\mathrm{b}$ in the paper (see discussion in Section 2.1).<br />
* '''kappa1''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _1$ in the paper (see definition in Section 2.1).<br />
* '''kappa2''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _2$ in the paper (see definition in Section 2.1).<br />
<br />
<br />
== References ==<br />
<br />
<References /></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Template:PlanckPapers&diff=11613Template:PlanckPapers2017-12-04T20:01:55Z<p>Mlopezca: </p>
<hr />
<div><noinclude><br />
==Description==<br />
The template <tt>PlanckPapers</tt> produces references for Planck papers in the text. There are three differences between this template and the [[Template:BibCite|BibCite]].<br />
*<tt>PlanckPapers</tt> creates in the text a direct link to the papers being cited;<br />
*With <tt>PlanckPapers</tt>, it is possible to create a link to a specific page of the paper;<br />
*With <tt>PlanckPapers</tt> it is possible to create a link with text defined by the user;<br />
The syntax of <tt>PlanckPapers</tt> is<br />
<pre><br />
{{PlanckPapers|label|page|link}}<br />
</pre><br />
where the thre parameters are as follows:<br />
;<tt>label</tt><br />
:label used to cite the paper in LaTeX;<br />
;<tt>page</tt><br />
:(optional parameter) page number where the paper paper should open when the user clicks on the link. When this parameter is not provided the paperwill open in page 1;<br />
;<tt>link</tt><br />
:(optional parameter) text of the link to the paper. If not supplied the default text is one of '''Planck-2013-'''X, '''Planck-Early-'''X or '''Planck-Int-'''X or '''Planck-PreLaunch-'''X, for the Planck 2013 papers, Planck early papers, Planck internediate papers or Planck pre launch papers. The X stands for a roman numeral identifying the paper being cited;<br />
<br />
==Examples==<br />
===Citing a paper using all the default values for optional parameters===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2013-p01}}<br />
</pre><br />
'''How it appears'''<br />
<br />
A very interesting result can be found in {{PlanckPapers|planck2013-p01}}.<br />
<br />
===Citing a paper and opening it in a given page===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2014-a01|10}}<br />
</pre><br />
'''How it appears'''<br />
<br />
On the other hand in {{PlanckPapers|planck2014-a01|10}} a demonstration is given of a most interesting fact.<br />
<br />
In this case the paper opens in page 10.<br />
<br />
===Citing a paper with a custom link===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2013-p09||isotropy and statistics paper}}<br />
</pre><br />
'''How it appears'''<br />
<br />
On the other hand it is shown in the {{PlanckPapers|planck2013-p09||isotropy and statistics paper}} the above mentioned result is rubish.<br />
<br />
Note that in this last case, the second argument (the page number where the paper should open) does not need to have a value and it is perfectly fine for the corresponding slot in the template syntax (delimited by the <tt>||</tt> in the template syntax) to be emtpy. It is however mandatory to use the two <tt>|</tt> in order for the template to be interpreted correctly.<br />
<br />
===Displaying the list of references at the end of the page===<br />
Have a look at [[Template:BibCite#Displaying_the_list_of_references_at_the_bottom_of_a_page|BibCite]].<br />
<br />
= References =<br />
<References /><br />
</noinclude><includeonly>{{#switch: {{{1}}}<br />
|planck2014-a01=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27101-15/aa27101-15.html#page={{{2|1}}} {{{3|Planck-2015-A01}}}]<br />
|planck2014-a03=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25818-15/aa25818-15.html#page={{{2|1}}} {{{3|Planck-2015-A02}}}]<br />
|planck2014-a04=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26998-15/aa26998-15.html#page={{{2|1}}} {{{3|Planck-2015-A03}}}]<br />
|planck2014-a05=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25809-15/aa25809-15.html#page={{{2|1}}} {{{3|Planck-2015-A04}}}]<br />
|planck2014-a06=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26632-15/aa26632-15.html#page={{{2|1}}} {{{3|Planck-2015-A05}}}]<br />
|planck2014-a07=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25813-15/aa25813-15.html#page={{{2|1}}} {{{3|Planck-2015-A06}}}]<br />
|planck2014-a08=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25844-15/aa25844-15.html#page={{{2|1}}} {{{3|Planck-2015-A07}}}]<br />
|planck2014-a09=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25820-15/aa25820-15.html#page={{{2|1}}} {{{3|Planck-2015-A08}}}]<br />
|planck2014-a11=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25936-15/aa25936-15.html#page={{{2|1}}} {{{3|Planck-2015-A09}}}]<br />
|planck2014-a12=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html#page={{{2|1}}} {{{3|Planck-2015-A10}}}]<br />
|planck2014-a13=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26926-15/aa26926-15.html#page={{{2|1}}} {{{3|Planck-2015-A11}}}]<br />
|planck2014-a14=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27103-15/aa27103-15.html#page={{{2|1}}} {{{3|Planck-2015-A12}}}]<br />
|planck2014-a15=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25830-15/aa25830-15.html#page={{{2|1}}} {{{3|Planck-2015-A13}}}]<br />
|planck2014-a16=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25814-15/aa25814-15.html#page={{{2|1}}} {{{3|Planck-2015-A14}}}]<br />
|planck2014-a17=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25941-15/aa25941-15.html#page={{{2|1}}} {{{3|Planck-2015-A15}}}]<br />
|planck2014-a18=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26681-15/aa26681-15.html#page={{{2|1}}} {{{3|Planck-2015-A16}}}]<br />
|planck2014-a19=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25836-15/aa25836-15.html#page={{{2|1}}} {{{3|Planck-2015-A17}}}]<br />
|planck2014-a20=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25829-15/aa25829-15.html#page={{{2|1}}} {{{3|Planck-2015-A18}}}]<br />
|planck2014-a22=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25821-15/aa25821-15.html#page={{{2|1}}} {{{3|Planck-2015-A19}}}]<br />
|planck2014-a24=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25898-15/aa25898-15.html#page={{{2|1}}} {{{3|Planck-2015-A20}}}]<br />
|planck2014-a26=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25831-15/aa25831-15.html#page={{{2|1}}} {{{3|Planck-2015-A21}}}]<br />
|planck2014-a28=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25826-15/aa25826-15.html#page={{{2|1}}} {{{3|Planck-2015-A22}}}]<br />
|planck2014-a29=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27418-15/aa27418-15.html#page={{{2|1}}} {{{3|Planck-2015-A23}}}]<br />
|planck2014-a30=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25833-15/aa25833-15.html#page={{{2|1}}} {{{3|Planck-2015-A24}}}]<br />
|planck2014-a31=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26803-15/aa26803-15.html#page={{{2|1}}} {{{3|Planck-2015-A25}}}]<br />
|planck2014-a33=[http://arxiv.org#page={{{2|1}}} {{{3|Planck-2015-A33}}}]<br />
|planck2014-a35=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26914-15/aa26914-15.html#page={{{2|1}}} {{{3|Planck-2015-A26}}}]<br />
|planck2014-a36=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25823-15/aa25823-15.html#page={{{2|1}}} {{{3|Planck-2015-A27}}}]<br />
|planck2014-a37=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25819-15/aa25819-15.html#page={{{2|1}}} {{{3|Planck-2015-A28}}}]<br />
|pb2015=[http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.101301#page={{{2|1}}} {{{3|Planck-BICEP}}}]<br />
|planck2014-XVIII=[http://www.aanda.org/articles/aa/pdf/2015/01/aa23836-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XVIII}}}]<br />
|planck2014-XIX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24082-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XIX}}}]<br />
|planck2014-XX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24086-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XX}}}]<br />
|planck2014-XXI=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24087-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXI}}}]<br />
|planck2014-XXII=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24088-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXII}}}]<br />
|planck2014-XXIII=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24434-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIII}}}]<br />
|planck2014-XXIV=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24496-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIV}}}]<br />
|planck2014-XXV=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24643-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXV}}}]<br />
|planck2014-XXVI=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24674-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVI}}}]<br />
|planck2014-XXVII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24790-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVII}}}]<br />
|planck2014-XXVIII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24955-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVIII}}}]<br />
|planck2014-XXIX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa24945-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIX}}}]<br />
|planck2014-XXX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25034-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXX}}}]<br />
|planck2014-XXXI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXI}}}]<br />
|planck2014-XXXII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25044-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXII}}}]<br />
|planck2014-XXXIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25305-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXIII}}}]<br />
|planck2015-XXXIV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25616-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXIV}}}]<br />
|planck2015-XXXV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25896-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXV}}}]<br />
|planck2015-XXXVI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26345-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVI}}}]<br />
|planck2015-XXXVII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26328-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVII}}}]<br />
|planck2015-XXXVIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26506-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVIII}}}]<br />
|planck2015-XXXIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27206-15/aa27206-15.html#page={{{2|1}}} {{{3|Planck-2015-XXXIX}}}]<br />
|planck2015-XL=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27743-15/aa27743-15.html#page={{{2|1}}} {{{3|Planck-2015-XL}}}]<br />
|planck2015-XLI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27932-15/aa27932-15.html#page={{{2|1}}} {{{3|Planck-2015-XLI}}}]<br />
|planck2016-XLII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28033-15/aa28033-15.html#page={{{2|1}}} {{{3|Planck-2016-XLII}}}]<br />
|planck2016-XLIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28522-16/aa28522-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIII}}}]<br />
|planck2016-XLIV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28636-16/aa28636-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIV}}}]<br />
|planck2016-XLV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27780-15/aa27780-15.html#page={{{2|1}}} {{{3|Planck-2016-XLV}}}]<br />
|planck2016-XLVI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28890-16/aa28890-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVI}}}]<br />
|planck2016-XLVII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28897-16/aa28897-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVII}}}]<br />
|planck2016-XLVIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29022-16/aa29022-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVIII}}}]<br />
|planck2016-XLIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29018-16/aa29018-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIX}}}]<br />
|planck2016-L=[https://www.aanda.org/articles/aa/abs/2017/03/aa29164-16/aa29164-16.html#page={{{2|1}}} {{{3|Planck-2016-L}}}]<br />
|planck2016-LI=[https://www.aanda.org/articles/aa/abs/2017/11/aa29504-16/aa29504-16.html#page={{{2|1}}} {{{3|Planck-2016-LI}}}]<br />
|planck2017-LII=[https://www.aanda.org/articles/aa/abs/2017/11/aa30311-16/aa30311-16.html#page={{{2|1}}} {{{3|Planck-2017-LII}}}]<br />
|planck2013-p01=[http://www.aanda.org/articles/aa/abs/2014/11/aa21529-13/aa21529-13.html#page={{{2|1}}} {{{3|Planck-2013-I}}}]<br />
|planck2013-p02=[http://www.aanda.org/articles/aa/abs/2014/11/aa21550-13/aa21550-13.html#page={{{2|1}}} {{{3|Planck-2013-II}}}] <br />
|planck2013-p02a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21574-13/aa21574-13.html#page={{{2|1}}} {{{3|Planck-2013-III}}}] <br />
|planck2013-p02d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21544-13/aa21544-13.html#page={{{2|1}}} {{{3|Planck-2013-IV}}}]<br />
|planck2013-p02b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21527-13/aa21527-13.html#page={{{2|1}}} {{{3|Planck-2013-V}}}]<br />
|planck2013-p03=[http://www.aanda.org/articles/aa/abs/2014/11/aa21570-13/aa21570-13.html#page={{{2|1}}} {{{3|Planck-2013-VI}}}]<br />
|planck2013-p03c=[http://www.aanda.org/articles/aa/abs/2014/11/aa21535-13/aa21535-13.html#page={{{2|1}}} {{{3|Planck-2013-VII}}}]<br />
|planck2013-p03b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21538-13/aa21538-13.html#page={{{2|1}}} {{{3|Planck-2013-VIII}}}]<br />
|planck2013-p03d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21531-13/aa21531-13.html#page={{{2|1}}} {{{3|Planck-2013-IX}}}]<br />
|planck2013-p03e=[http://www.aanda.org/articles/aa/abs/2014/11/aa21577-13/aa21577-13.html#page={{{2|1}}} {{{3|Planck-2013-X}}}]<br />
|planck2013-p06b=[http://www.aanda.org/articles/aa/abs/2014/11/aa23195-13/aa23195-13.html#page={{{2|1}}} {{{3|Planck-2013-XI}}}]<br />
|planck2013-p06=[http://www.aanda.org/articles/aa/abs/2014/11/aa21580-13/aa21580-13.html#page={{{2|1}}} {{{3|Planck-2013-XII}}}]<br />
|planck2013-p03a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21553-13/aa21553-13.html#page={{{2|1}}} {{{3|Planck-2013-XIII}}}]<br />
|planck2013-pip88=[http://www.aanda.org/articles/aa/abs/2014/11/aa21562-13/aa21562-13.html#page={{{2|1}}} {{{3|Planck-2013-XIV}}}]<br />
|planck2013-p08=[http://www.aanda.org/articles/aa/abs/2014/11/aa21573-13/aa21573-13.html#page={{{2|1}}} {{{3|Planck-2013-XV}}}]<br />
|planck2013-p11=[http://www.aanda.org/articles/aa/abs/2014/11/aa21591-13/aa21591-13.html#page={{{2|1}}} {{{3|Planck-2013-XVI}}}]<br />
|planck2013-p12=[http://www.aanda.org/articles/aa/abs/2014/11/aa21543-13/aa21543-13.html#page={{{2|1}}} {{{3|Planck-2013-XVII}}}]<br />
|planck2013-p13=[http://www.aanda.org/articles/aa/abs/2014/11/aa21540-13/aa21540-13.html#page={{{2|1}}} {{{3|Planck-2013-XVIII}}}]<br />
|planck2013-p14=[http://www.aanda.org/articles/aa/abs/2014/11/aa21526-13/aa21526-13.html#page={{{2|1}}} {{{3|Planck-2013-XIX}}}]<br />
|planck2013-p15=[http://www.aanda.org/articles/aa/abs/2014/11/aa21521-13/aa21521-13.html#page={{{2|1}}} {{{3|Planck-2013-XX}}}]<br />
|planck2013-p05b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21522-13/aa21522-13.html#page={{{2|1}}} {{{3|Planck-2013-XXI}}}]<br />
|planck2013-p17=[http://www.aanda.org/articles/aa/abs/2014/11/aa21569-13/aa21569-13.html#page={{{2|1}}} {{{3|Planck-2013-XXII}}}]<br />
|planck2013-p09=[http://www.aanda.org/articles/aa/abs/2014/11/aa21534-13/aa21534-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIII}}}]<br />
|planck2013-p09a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21554-13/aa21554-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIV}}}]<br />
|planck2013-p20=[http://www.aanda.org/articles/aa/abs/2014/11/aa21621-13/aa21621-13.html#page={{{2|1}}} {{{3|Planck-2013-XXV}}}]<br />
|planck2013-p19=[http://www.aanda.org/articles/aa/abs/2014/11/aa21546-13/aa21546-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVI}}}]<br />
|planck2013-pipaberration=[http://www.aanda.org/articles/aa/abs/2014/11/aa21556-13/aa21556-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVII}}}]<br />
|planck2013-p05=[http://www.aanda.org/articles/aa/abs/2014/11/aa21524-13/aa21524-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVIII}}}]<br />
|planck2013-p05a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21523-13/aa21523-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIX}}}]<br />
|planck2013-pip56=[http://www.aanda.org/articles/aa/abs/2014/11/aa22093-13/aa22093-13.html#page={{{2|1}}} {{{3|Planck-2013-XXX}}}]<br />
|planck2013-p01a=[http://www.aanda.org/articles/aa/abs/2014/11/aa23743-14/aa23743-14.html#page={{{2|1}}} {{{3|Planck-2013-XXXI}}}]<br />
|planck2013-p28=[http://www.sciops.esa.int/wikiSI/planckpla/index.php?title=Main_Page '''The Explanatory Supplement to the Planck 2013 results'''], Planck Collaboration, ESA, (2013).<br />
|planck2012-I=[http://www.aanda.org/articles/aa/pdf/2012/07/aa18731-11.pdf#page={{{2|1}}} {{{3|Planck-Int-I}}}]<br />
|planck2012-II=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19361-12.pdf#page={{{2|1}}} {{{3|Planck-Int-II}}}]<br />
|planck2012-III=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19398-12.pdf#page={{{2|1}}} {{{3|Planck-Int-III}}}]<br />
|planck2012-IV=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19519-12.pdf#page={{{2|1}}} {{{3|Planck-Int-IV}}}]<br />
|planck2012-V=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20040-12.pdf#page={{{2|1}}} {{{3|Planck-Int-V}}}]<br />
|planck2012-VI=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20039-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VI}}}]<br />
|planck2012-VII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20053-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VII}}}]<br />
|planck2012-VIII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20194-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VIII}}}]<br />
|planck2012-IX=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20271-12.pdf#page={{{2|1}}} {{{3|Planck-Int-IX}}}]<br />
|planck2012-X=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20247-12.pdf#page={{{2|1}}} {{{3|Planck-Int-X}}}]<br />
|planck2012-XI=[http://www.aanda.org/articles/aa/pdf/2013/09/aa20941-12.pdf#page={{{2|1}}} {{{3|Planck-Int-XI}}}]<br />
|planck2013-XII=[http://www.aanda.org/articles/aa/pdf/2013/09/aa21160-13.pdf#page={{{2|1}}} {{{3|Planck-Int-XII}}}]<br />
|planck2013-XIII=[http://www.aanda.org/articles/aa/pdf/2014/01/aa21299-13.pdf#page={{{2|1}}} {{{3|Planck-Int-XIII}}}]<br />
|planck2013-XIV=[http://arxiv.org/pdf/1307.6815.pdf#page={{{2|1}}} {{{3|Planck-Int-XIV}}}] <br />
|planck2013-XV=[http://arxiv.org/pdf/1309.1357.pdf#page={{{2|1}}} {{{3|Planck-Int-XV}}}]<br />
|planck2013-XVI=[http://arxiv.org/pdf/1311.1657.pdf#page={{{2|1}}} {{{3|Planck-Int-XVI}}}]<br />
|planck2013-XVII=[http://arxiv.org/pdf/1312.5446.pdf#page={{{2|1}}} {{{3|Planck-Int-XVII}}}]<br />
|planck2011-1-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16464-11.pdf#page={{{2|1}}} {{{3|Planck-Early-I}}}]<br />
|planck2011-1-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16486-11.pdf#page={{{2|1}}} {{{3|Planck-Early-II}}}]<br />
|planck2011-1-4=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16480-11.pdf#page={{{2|1}}} {{{3|Planck-Early-III}}}]<br />
|planck2011-1-5=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16487-11.pdf#page={{{2|1}}} {{{3|Planck-Early-IV}}}]<br />
|planck2011-1-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16484-11.pdf#page={{{2|1}}} {{{3|Planck-Early-V}}}]<br />
|planck2011-1-7=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16462-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VI}}}]<br />
|planck2011-1-10=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16474-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VII}}}]<br />
|planck2011-1-10sup=[http://arxiv.org {{{3|Planck-Exp-Sup}}}]<br />
|planck2011-5-1a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16459-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VIII}}}]<br />
|planck2011-5-1b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16460-11.pdf#page={{{2|1}}} {{{3|Planck-Early-IX}}}]<br />
|planck2011-5-2a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16457-11.pdf#page={{{2|1}}} {{{3|Planck-Early-X}}}]<br />
|planck2011-5-2b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16458-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XI}}}]<br />
|planck2011-5-2c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16489-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XII}}}]<br />
|planck2011-6-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16471-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIII}}}]<br />
|planck2011-6-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16475-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIV}}}]<br />
|planck2011-6-3a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16466-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XV}}}]<br />
|planck2011-6-4a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16454-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVI}}}]<br />
|planck2011-6-4b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16473-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVII}}}]<br />
|planck2011-6-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16461-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVIII}}}]<br />
|planck2011-7-0=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16479-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIX}}}]<br />
|planck2011-7-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16470-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XX}}}]<br />
|planck2011-7-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16455-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXI}}}]<br />
|planck2011-7-7a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16481-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXII}}}]<br />
|planck2011-7-7b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16472-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXIII}}}]<br />
|planck2011-7-12=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16485-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXIV}}}]<br />
|planck2011-7-13=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16483-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXV}}}]<br />
|planck2011-5-1c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa17430-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXVI}}}]<br />
|planck2011-6-3b=[http://www.aanda.org/articles/aa/pdf/2012/05/aa17825-11.pdf#page={{{2|1}}} {{{3|Planck-Swift-Fermi}}}]<br />
|ade2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13039-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-I}}}]<br />
|bersanelli2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12853-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-II}}}]<br />
|lamarre2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12975-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-III}}}]<br />
|leahy2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12855-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-IV}}}]<br />
|maffei2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12999-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-V}}}]<br />
|mandolesi2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12837-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VI}}}]<br />
|mennella2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12849-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VII}}}]<br />
|pajot2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13203-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VIII}}}]<br />
|rosset2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13054-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-IX}}}]<br />
|sandri2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12891-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-X}}}]<br />
|tauber2010a=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12983-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-XI}}}]<br />
|tauber2010b=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12911-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-XII}}}]<br />
|villa2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12860-09.pdf#page={{{2|1}}}} {{{3|Planck-PreLaunch-XIII}}}]<br />
}}{{#tag:ref|{{#switch: {{{1}}}<br />
|planck2014-a01=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27101-15/aa27101-15.html'''Planck 2015 results. I. Overview of products and results'''], Planck Collaboration, 2016, A&amp;A, 594, A1.<br />
|planck2014-a03=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25818-15/aa25818-15.html'''Planck 2015 results. II. LFI processing'''], Planck Collaboration, 2016, A&amp;A, 594, A2.<br />
|planck2014-a04=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26998-15/aa26998-15.html'''Planck 2015 results. III. LFI systematics'''], Planck Collaboration, 2016, A&amp;A, 594, A3.<br />
|planck2014-a05=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25809-15/aa25809-15.html'''Planck 2015 results. IV. LFI beams and window functions'''], Planck Collaboration, 2016, A&amp;A, 594, A4.<br />
|planck2014-a06=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26632-15/aa26632-15.html'''Planck 2015 results. V. LFI calibration'''], Planck Collaboration, 2016, A&amp;A, 594, A5.<br />
|planck2014-a07=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25813-15/aa25813-15.html'''Planck 2015 results. VI. LFI mapmaking'''], Planck Collaboration, 2016, A&amp;A, 594, A6.<br />
|planck2014-a08=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25844-15/aa25844-15.html'''Planck 2015 results. VII. High Frequency Instrument data processing: Time-ordered information and beam processing'''], Planck Collaboration, 2016, A&amp;A, 594, A7.<br />
|planck2014-a09=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25820-15/aa25820-15.html'''Planck 2015 results. VIII. High Frequency Instrument data processing: Calibration and maps'''], Planck Collaboration, 2016, A&amp;A, 594, A8.<br />
|planck2014-a11=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25936-15/aa25936-15.html'''Planck 2015 results. XI. Diffuse component separation: CMB maps'''], Planck Collaboration, 2016, A&amp;A, 594, A9.<br />
|planck2014-a12=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html'''Planck 2015 results. X. Diffuse component separation: Foreground maps'''], Planck Collaboration, 2016, A&amp;A, 594, A10.<br />
|planck2014-a13=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26926-15/aa26926-15.html'''Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of cosmological parameters'''], Planck Collaboration, 2016, A&amp;A, 594, A11.<br />
|planck2014-a14=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27103-15/aa27103-15.html'''Planck 2015 results. XII. Full Focal Plane Simulations'''], Planck Collaboration, 2016, A&amp;A, 594, A12.<br />
|planck2014-a15=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25830-15/aa25830-15.html'''Planck 2015 results. XIII. Cosmological parameters'''], Planck Collaboration, 2016, A&amp;A, 594, A13.<br />
|planck2014-a16=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25814-15/aa25814-15.html'''Planck 2015 results. XIV. Dark energy and modified gravity'''], Planck Collaboration, 2016, A&amp;A, 594, A14.<br />
|planck2014-a17=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25941-15/aa25941-15.html'''Planck 2015 results. XV. Gravitational Lensing'''], Planck Collaboration, 2016, A&amp;A, 594, A15.<br />
|planck2014-a18=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26681-15/aa26681-15.html'''Planck 2015 results. XVI. Isotropy and statistics'''], Planck Collaboration, 2016, A&amp;A, 594, A16.<br />
|planck2014-a19=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25836-15/aa25836-15.html'''Planck 2015 results. XVII. Primordial non-Gaussianity'''], Planck Collaboration, 2016, A&amp;A, 594, A17.<br />
|planck2014-a20=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25829-15/aa25829-15.html'''Planck 2015 results. XVIII. Background geometry and topology of the Universe'''], Planck Collaboration, 2016, A&amp;A, 594, A18.<br />
|planck2014-a22=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25821-15/aa25821-15.html'''Planck 2015 results. XIX. Constraints on primordial magnetic fields'''], Planck Collaboration, 2016, A&amp;A, 594, A19.<br />
|planck2014-a24=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25898-15/aa25898-15.html'''Planck 2015 results. XX. Constraints on inflation'''], Planck Collaboration, 2016, A&amp;A, 594, A20.<br />
|planck2014-a26=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25831-15/aa25831-15.html'''Planck 2015 results. XXI. The integrated Sachs-Wolfe effect'''], Planck Collaboration, 2016, A&amp;A, 594, A21.<br />
|planck2014-a28=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25826-15/aa25826-15.html'''Planck 2015 results. XXII. A map of the thermal Sunyaev-Zeldovich effect'''], Planck Collaboration, 2016, A&amp;A, 594, A22.<br />
|planck2014-a29=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27418-15/aa27418-15.html'''Planck 2015 results. XXIII. Thermal Sunyaev-Zeldovich effect–cosmic infrared background correlation'''], Planck Collaboration, 2016, A&amp;A, 594, A23.<br />
|planck2014-a30=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25833-15/aa25833-15.html'''Planck 2015 results. XXIV. Cosmology from Sunyaev-Zeldovich cluster counts '''], Planck Collaboration, 2016, A&amp;A, 594, A24.<br />
|planck2014-a31=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26803-15/aa26803-15.html'''Planck 2015 results. XXV. Diffuse low frequency Galactic foregrounds'''], Planck Collaboration, 2016, A&amp;A, 594, A25.<br />
|planck2014-a33=[http://arxiv.org '''A33 Zodiacal light'''], Planck Collaboration C33, in preparation, (2015).<br />
|planck2014-a35=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26914-15/aa26914-15.html'''Planck 2015 results. XXVI. The second Planck catalogue of compact sources'''], Planck Collaboration, 2016, A&amp;A, 594, A26.<br />
|planck2014-a36=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25823-15/aa25823-15.html'''Planck 2015 results. XXVII. The second Planck catalogue of Sunyaev-Zeldovich sources'''], Planck Collaboration, 2016, A&amp;A, 594, A27.<br />
|planck2014-a37=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25819-15/aa25819-15.html'''Planck 2015 results. XXVIII. The Planck catalogue of Galactic cold clumps'''], Planck Collaboration, 2016, A&amp;A, 594, A28.<br />
|pb2015=[http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.101301'''A joint analysis of BICEP2/Keck Array and Planck data'''], BICEP2/Keck Array, Planck Collaborations, PRL, 114, 101301 (2015).<br />
|planck2014-XVIII=[http://www.aanda.org/articles/aa/pdf/2015/01/aa23836-14.pdf'''Planck intermediate results. XVIII. The millimetre and submillimetre emission from planetary nebulae'''], Planck Collaboration Int. XVIII, A&A, '''573''', A6, (2015).<br />
|planck2014-XIX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24082-14.pdf'''Planck intermediate results. XIX. An overview of the polarized thermal emission from Galactic dust'''], Planck Collaboration Int. XIX, A&A, '''576''', A104, (2015).<br />
|planck2014-XX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24086-14.pdf'''Planck intermediate results. XX. Comparison of polarized thermal emission from Galactic dust with simulations of MHD turbulence'''], Planck Collaboration Int. XX, A&A, '''576''', A105, (2015).<br />
|planck2014-XXI=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24087-14.pdf'''Planck intermediate results. XXI. Comparison of polarized thermal emission from Galactic dust at 353 GHz with optical interstellar polarization'''], Planck Collaboration Int. XXI, A&A, '''576''', A106, (2015).<br />
|planck2014-XXII=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24088-14.pdf '''Planck intermediate results. XXII. Frequency dependence of thermal emission from Galactic dust in intensity and polarization'''], Planck Collaboration Int. XXII, A&A, '''576''', A107, (2015).<br />
|planck2014-XXIII=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24434-14.pdf '''Planck intermediate results. XXIII. Galactic plane emission components derived from Planck with ancillary data'''], Planck Collaboration Int. XXIII, A&A, '''580''', A13, (2015).<br />
|planck2014-XXIV=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24496-14.pdf'''Planck intermediate results. XXIV. Constraints on variation of fundamental constants'''], Planck Collaboration Int. XXIV, A&A, '''580''', A22, (2015).<br />
|planck2014-XXV=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24643-14.pdf'''Planck intermediate results. XXV. The Andromeda Galaxy as seen by ''Planck'''''], Planck Collaboration Int. XXV, A&A, '''582''', A28, (2015).<br />
|planck2014-XXVI=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24674-14.pdf '''Planck intermediate results. XXVI. Optical identification and redshifts of ''Planck'' clusters with the RTT150 telescope'''], Planck Collaboration Int. XXVI, A&A, '''582''', A29, (2015).<br />
|planck2014-XXVII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24790-14.pdf'''Planck intermediate results. XXVII. High-redshift infrared galaxy overdensity candidates and lensed sources discovered by Planck and confirmed by Herschel-SPIRE'''], Planck Collaboration Int. XXVII, A&A, '''582''', A29, (2015).<br />
|planck2014-XXVIII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24955-14.pdf'''Planck intermediate results. XXVIII. Interstellar gas and dustin the Chamaeleon clouds as seen by ''Fermi'' LAT and ''Planck'''''], Planck Collaboration Int. XXVIII, A&A, '''582''', A30, (2015).<br />
|planck2014-XXIX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf''Planck intermediate results. XXIX. All-sky dust modelling with ''Planck'', IRAS, and WISE observations'''], Planck Collaboration Int. XXIX, A&A, '''586''', A132, (2016).<br />
|planck2014-XXX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25034-14.pdf'''Planck intermediate results. XXX. The angular power spectrum of polarized dust emission at intermediate and high Galactic latitudes'''], Planck Collaboration Int. XXX, A&A, '''586''', A133, (2016).<br />
|planck2014-XXXI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf'''Planck intermediate results. XXXI. Microwave survey of Galactic supernova remnants'''], Planck Collaboration Int. XXXI, A&A, '''586''', A134, (2016).<br />
|planck2014-XXXII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25044-14.pdf'''Planck intermediate results. XXXII. The relative orientation between the magnetic field and structures traced by interstellar dust'''], Planck Collaboration Int. XXXII, A&A, '''586''', A135, (2016).<br />
|planck2014-XXXIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25305-14.pdf'''Planck intermediate results. XXXIII. Signature of the magnetic field geometry of interstellar filaments in dust polarization maps'''], Planck Collaboration Int. XXXIII, A&A, '''586''', A136, (2016).<br />
|planck2015-XXXIV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25616-15.pdf'''Planck intermediate results. XXXIV. The magnetic field structure in the Rosette Nebula'''], Planck Collaboration Int. XXX A&A, 586, A137, (2016).<br />
|planck2015-XXXV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25896-15.pdf'''Planck intermediate results. XXXV. Probing the role of the magnetic field in the formation of structure in molecular clouds'''], Planck Collaboration Int. XXX A&A, 586, A138, (2016).<br />
|planck2015-XXXVI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26345-15.pdf'''Planck intermediate results. XXXVI. Optical identification and redshifts of Planck SZ sources with telescopes in the Canary Islands Observatories'''], Planck Collaboration Int. XXX A&A, 586, A139, (2016).<br />
|planck2015-XXXVII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26328-15.pdf'''Planck intermediate results. XXXVII. Evidence of unbound gas from the kinetic Sunyaev-Zeldovich effect.'''], Planck Collaboration Int. XXX A&A, 586, A140, (2016).<br />
|planck2015-XXXVIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26506-15.pdf'''Planck intermediate results. XXXVIII. E- and B-modes of dust polarization from the magnetized filamentary structure of the interstellar medium'''], Planck Collaboration Int. XXX A&A, 586, A141, (2016).<br />
<br />
|planck2015-XXXIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27206-15/aa27206-15.html'''Planck intermediate results. XXXIX. The Planck list of high-redshift source candidates'''], Planck Collaboration Int. XXXIX A&A, 596, A100, (2016).<br />
|planck2015-XL=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27743-15/aa27743-15.html'''Planck intermediate results. XL. The Sunyaev-Zeldovich signal from the Virgo cluster'''], Planck Collaboration Int. XL A&A, 596, A101, (2016).<br />
|planck2015-XLI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27932-15/aa27932-15.html'''Planck intermediate results. XLI. A map of lensing-induced B-modes'''], Planck Collaboration Int. XLI A&A, 596, A102, (2016).<br />
|planck2016-XLII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28033-15/aa28033-15.html'''Planck intermediate results. XLII. Large-scale Galactic magnetic fields'''], Planck Collaboration Int. XLII A&A, 596, A103, (2016).<br />
|planck2016-XLIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28522-16/aa28522-16.html'''Planck intermediate results. XLIII. The spectral energy distribution of dust in clusters of galaxies'''], Planck Collaboration Int. XLIII A&A, 596, A104, (2016).<br />
|planck2016-XLIV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28636-16/aa28636-16.html'''Planck intermediate results. XLIV. The structure of the Galactic magnetic field from dust polarization maps of the southern Galactic cap'''], Planck Collaboration Int. XLIV A&A, 596, A105, (2016).<br />
|planck2016-XLV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27780-15/aa27780-15.html'''Planck intermediate results. XLV. Radio spectra of northern extragalactic radio sources'''], Planck Collaboration Int. XLV A&A, 596, A106, (2016).<br />
|planck2016-XLVI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28890-16/aa28890-16.html'''Planck intermediate results. XLVI. Reduction of large-scale systematic effects in HFI polarization maps and estimation of the reionization optical depth'''], Planck Collaboration Int. XLVI A&A, 596, A107, (2016).<br />
|planck2016-XLVII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28897-16/aa28897-16.html'''Planck intermediate results. XLVII. Constraints on reionization history'''], Planck Collaboration Int. XLVII A&A, 596, A108, (2016).<br />
|planck2016-XLVIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29022-16/aa29022-16.html'''Planck intermediate results. XLVIII. Disentangling Galactic dust emission and cosmic infrared background anisotropies'''], Planck Collaboration Int. XLVIII A&A, 596, A109, (2016).<br />
|planck2016-XLIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29018-16/aa29018-16.html'''Planck intermediate results. XLIX. Parity-violation constraints from polarization data'''], Planck Collaboration Int. XLIX A&A, 596, A110, (2016).<br />
|planck2016-L=[http://xxx.lanl.gov/abs/1606.07335'''Planck intermediate results. L. Evidence for spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B-mode analysis'''], Planck Collaboration Int. L A&A, 599, A55, (2017)<br />
|planck2016-LI=[http://xxx.lanl.gov/abs/1608.02487'''Planck intermediate results. LI. Features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters'''], Planck Collaboration Int. LI A&A, 607, A95, (2017)<br />
|planck2017-LII=[https://www.aanda.org/articles/aa/abs/2017/11/aa30311-16/aa30311-16.html'''Planck intermediate results. LII. Planets flux densities'''], Planck Collaboration Int. LII A&A, 607, A122, (2017)<br />
|planck2013-p01=[http://www.aanda.org/articles/aa/abs/2014/11/aa21529-13/aa21529-13.html#page={{{2|1}}}'''Planck 2013 results. I. Overview of Products and Results'''], Planck Collaboration, 2014, A&A, 571, A1<br />
|planck2013-p02=[http://www.aanda.org/articles/aa/abs/2014/11/aa21550-13/aa21550-13.html#page={{{2|1}}}'''Planck 2013 results. II. Low Frequency Instrument data processing'''], Planck Collaboration, 2014, A&A, 571, A2<br />
|planck2013-p02a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21574-13/aa21574-13.html#page={{{2|1}}}'''Planck 2013 results. III. Low Frequency Instrument systematic uncertainties'''], Planck Collaboration, 2014, A&A, 571, A3<br />
|planck2013-p02d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21544-13/aa21544-13.html#page={{{2|1}}}'''Planck 2013 results. IV. Low Frequency Instrument beams and window functions'''], Planck Collaboration, 2014, A&A, 571, A4<br />
|planck2013-p02b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21527-13/aa21527-13.html#page={{{2|1}}}'''Planck 2013 results. V. LFI Calibration'''], Planck Collaboration, 2014, A&A, 571, A5<br />
|planck2013-p03=[http://www.aanda.org/articles/aa/abs/2014/11/aa21570-13/aa21570-13.html#page={{{2|1}}} '''Planck 2013 results. VI. High Frequency Instrument Data Processing'''], Planck Collaboration, 2014, A&A, 571, A6<br />
|planck2013-p03c=[http://www.aanda.org/articles/aa/abs/2014/11/aa21535-13/aa21535-13.html#page={{{2|1}}}'''Planck 2013 results. VII. HFI time response and beams'''], Planck Collaboration, 2014, A&A, 571, A7<br />
|planck2013-p03b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21538-13/aa21538-13.html#page={{{2|1}}}'''Planck 2013 results. VIII. HFI photometric calibration and Map-making'''], Planck Collaboration, 2014, A&A, 571, A8<br />
|planck2013-p03d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21531-13/aa21531-13.html#page={{{2|1}}}'''Planck 2013 results. IX. HFI spectral response'''], Planck Collaboration, 2014, A&A, 571, A9<br />
|planck2013-p03e=[http://www.aanda.org/articles/aa/abs/2014/11/aa21577-13/aa21577-13.html#page={{{2|1}}}'''Planck 2013 results. X. HFI energetic particle effects: characterization, removal, and simulation'''], Planck Collaboration, 2014, A&A, 571, A10<br />
|planck2013-p06=[http://www.aanda.org/articles/aa/abs/2014/11/aa23195-13/aa23195-13.html#page={{{2|1}}}'''Planck 2013 results. XI. Component separation'''], Planck Collaboration, 2014, A&A, 571, A11<br />
|planck2013-p06b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21580-13/aa21580-13.html#page={{{2|1}}}'''Planck 2013 results. XII. All-sky model of thermal dust emission'''], Planck Collaboration, 2014, A&A, 571, A12<br />
|planck2013-p03a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21553-13/aa21553-13.html#page={{{2|1}}}'''Planck 2013 results. XIII. Galactic CO emission''], Planck Collaboration, 2014, A&A, 571, A13<br />
|planck2013-pip88=[http://www.aanda.org/articles/aa/abs/2014/11/aa21562-13/aa21562-13.html#page={{{2|1}}}'''Planck 2013 results. XIV. Zodiacal emission'''], Planck Collaboration, 2014, A&A, 571, A14<br />
|planck2013-p08=[http://www.aanda.org/articles/aa/abs/2014/11/aa21573-13/aa21573-13.html#page={{{2|1}}}'''Planck 2013 results. XV. CMB power spectra and likelihood'''], Planck Collaboration, 2014, A&A, 571, A15<br />
|planck2013-p11=[http://www.aanda.org/articles/aa/abs/2014/11/aa21591-13/aa21591-13.html#page={{{2|1}}}'''Planck 2013 results. XVI. Cosmological parameters'''], Planck Collaboration, 2014, A&A, 571, A16<br />
|planck2013-p12=[http://www.aanda.org/articles/aa/abs/2014/11/aa21543-13/aa21543-13.html#page={{{2|1}}}'''Planck 2013 results. XVII. Gravitational lensing by large-scale structure'''], Planck Collaboration, 2014, A&A, 571, A17<br />
|planck2013-p13=[http://www.aanda.org/articles/aa/abs/2014/11/aa21540-13/aa21540-13.html#page={{{2|1}}}'''Planck 2013 results. XVIII. The gravitational lensing-infrared background correlation'''], Planck Collaboration, 2014, A&A, 571, A18<br />
|planck2013-p14=[http://www.aanda.org/articles/aa/abs/2014/11/aa21526-13/aa21526-13.html#page={{{2|1}}}'''Planck 2013 results. XIX. The integrated Sachs-Wolfe effect'''], Planck Collaboration, 2014, A&A, 571, A19<br />
<br />
|planck2013-p15=[http://www.aanda.org/articles/aa/abs/2014/11/aa21521-13/aa21521-13.html#page={{{2|1}}}'''Planck 2013 results. XX. Cosmology from Planck Sunyaev Zeldovich cluster counts'''], Planck Collaboration, 2014, A&A, 571, A20<br />
|planck2013-p05b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21522-13/aa21522-13.html#page={{{2|1}}}'''Planck 2013 results. XXI. Power spectrum and high-order statistics of the Planck all-sky Compton parameter map'''], Planck Collaboration, 2014, A&A, 571, A21<br />
|planck2013-p17=[http://www.aanda.org/articles/aa/abs/2014/11/aa21569-13/aa21569-13.html#page={{{2|1}}}'''Planck 2013 results. XXII. Constraints on inflation'''], Planck Collaboration, 2014, A&A, 571, A22<br />
|planck2013-p09=[http://www.aanda.org/articles/aa/abs/2014/11/aa21534-13/aa21534-13.html#page={{{2|1}}}'''Planck 2013 results. XXIII. Isotropy and statistics of the CMB'''], Planck Collaboration, 2014, A&A, 571, A23<br />
|planck2013-p09a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21554-13/aa21554-13.html#page={{{2|1}}}'''Planck 2013 results. XXIV. Constraints on primordial non-Gaussianity'''], Planck Collaboration, 2014, A&A, 571, A24<br />
|planck2013-p20=[http://www.aanda.org/articles/aa/abs/2014/11/aa21621-13/aa21621-13.html#page={{{2|1}}}'''Planck 2013 results. XXV. Searches for cosmic strings and other topological defects'''], Planck Collaboration, 2014, A&A, 571, A25<br />
|planck2013-p19=[http://www.aanda.org/articles/aa/abs/2014/11/aa21546-13/aa21546-13.html#page={{{2|1}}}'''Planck 2013 results. XXVI. Geometry and topology of the Universe'''], Planck Collaboration, 2014, A&A, 571, A26<br />
|planck2013-pipaberration=[http://www.aanda.org/articles/aa/abs/2014/11/aa21556-13/aa21556-13.html#page={{{2|1}}}'''Planck 2013 results. XXVII. Doppler boosting of the CMB: Eppur si muove'''], Planck Collaboration, 2014, A&A, 571, A27<br />
|planck2013-p05=[http://www.aanda.org/articles/aa/abs/2014/11/aa21524-13/aa21524-13.html#page={{{2|1}}}'''Planck 2013 results. XXVIII. The Planck Catalogue of Compact Sources'''], Planck Collaboration, 2014, A&A, 571, A28<br />
|planck2013-p05a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21523-13/aa21523-13.html#page={{{2|1}}}'''Planck 2013 results. XXIX. The Planck Catalogue of Sunyaev-Zeldovich sources'''], Planck Collaboration, 2014, A&A, 571, A29<br />
|planck2013-pip56=[http://www.aanda.org/articles/aa/abs/2014/11/aa22093-13/aa22093-13.html#page={{{2|1}}}'''Planck 2013 results. XXX. Cosmic infrared background measurements and implications for star formation'''], Planck Collaboration, 2014, A&A, 571, A30<br />
|planck2013-p01a=[http://www.aanda.org/articles/aa/pdf/2014/11/aa23743-14.pdf#page={{{2|1}}}'''Planck 2013 results. XXXI. Consistency of Planck data'''], Planck Collaboration, 2014, A&A, 571, A31<br />
|planck2013-p28=[http://www.sciops.esa.int/wikiSI/planckpla/index.php?title=Main_Page '''The Explanatory Supplement to the Planck 2013 results'''], Planck Collaboration, ESA, (2013).<br />
|planck2012-I=[http://www.aanda.org/articles/aa/pdf/2012/07/aa18731-11.pdf#page={{{2|1}}} '''Planck intermediate results. I. Further validation of new Planck clusters with XMM-Newton'''], Planck Collaboration Int. I, A&A, '''543''', A102, (2012).<br />
|planck2012-II=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19361-12.pdf#page={{{2|1}}}'''Planck intermediate results. II. Comparison of Sunyaev-Zeldovich measurements from Planck and from the Arcminute Microkelvin Imager for 11 galaxy clusters'''], Planck, AMI Collaborations, A&A, '''550''', A128, (2013).<br />
|planck2012-III=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19398-12.pdf#page={{{2|1}}}'''Planck intermediate results. III. The relation between galaxy cluster mass and Sunyaev-Zeldovich signal'''], Planck Collaboration Int. III, A&A, '''550''', A129, (2013).<br />
|planck2012-IV=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19519-12.pdf#page={{{2|1}}}'''Planck intermediate results. IV. The XMM-Newton validation programme for new Planck clusters'''], Planck Collaboration Int. IV, A&A, '''550''', A130, (2013).<br />
|planck2012-V=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20040-12.pdf#page={{{2|1}}}'''Planck intermediate results. V. Pressure profiles of galaxy clusters from the Sunyaev-Zeldovich effect'''], Planck Collaboration Int. V, A&A, '''550''', A131, (2013).<br />
|planck2012-VI=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20039-12.pdf#page={{{2|1}}}'''Planck intermediate results. VI. The dynamical structure of PLCKG214.6+37.0, a Planck discovered triple system of galaxy clusters'''], Planck Collaboration Int. VI, A&A, '''550''', A132, (2013).<br />
|planck2012-VII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20053-12.pdf#page={{{2|1}}}'''Planck intermediate results. VII. Statistical properties of infrared and radio extragalactic sources from the Planck Early Release Compact Source Catalogue at frequencies between 100 and 857 GHz'''], Planck Collaboration Int. VII, A&A, '''550''', A133, (2013).<br />
|planck2012-VIII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20194-12.pdf#page={{{2|1}}}'''Planck intermediate results. VIII. Filaments between interacting clusters'''], Planck Collaboration Int. VIII, A&A, '''550''', A134, (2013).<br />
|planck2012-IX=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20271-12.pdf#page={{{2|1}}}'''Planck intermediate results. IX. Detection of the Galactic haze with Planck'''], Planck Collaboration Int. IX, A&A, '''554''', A139, (2013).<br />
|planck2012-X=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20247-12.pdf#page={{{2|1}}}'''Planck intermediate results. X. Physics of the hot gas in the Coma cluster'''], Planck Collaboration Int. X, A&A, '''554''', A140, (2013).<br />
|planck2012-XI=[http://www.aanda.org/articles/aa/pdf/2013/09/aa20941-12.pdf#page={{{2|1}}}'''Planck intermediate results. XI. The gas content of dark matter halos: the Sunyaev-Zeldovich-stellar mass relation for locally brightest galaxies'''], Planck Collaboration Int. XI, A&A, '''557''', A52, (2013).<br />
|planck2013-XII=[http://www.aanda.org/articles/aa/pdf/2013/09/aa21160-13.pdf#page={{{2|1}}}'''Planck intermediate results. XII. Diffuse Galactic components in the Gould Belt System'''], Planck Collaboration Int. XII, A&A, '''557''', A53, (2013).<br />
|planck2013-XIII=[http://www.aanda.org/articles/aa/pdf/2014/01/aa21299-13.pdf#page={{{2|1}}}'''Planck intermediate results. XIII. Constraints on peculiar velocities'''], Planck Collaboration Int. XIII, A&A, '''561''', A97, (2013).<br />
|planck2013-XIV=[http://arxiv.org/pdf/1307.6815.pdf#page={{{2|1}}}'''Planck intermediate results. XIV. Dust emission at millimetre wavelengths in the Galactic plane'''], Planck Collaboration Int. XIV, A&A, in press, (2014).<br />
|planck2013-XV=[http://arxiv.org/pdf/1309.1357.pdf#page={{{2|1}}}'''Planck intermediate results. XV. A study of anomalous microwave emission in Galactic clouds'''], Planck Collaboration Int. XV, A&A, in press, (2014).<br />
|planck2013-XVI=[http://arxiv.org/pdf/1311.1657.pdf#page={{{2|1}}}'''Planck intermediate results. XVI. Profile likelihoods for cosmological parameters'''], Planck Collaboration Int. XVI, A&A, in press, (2014).<br />
|planck2013-XVII=[http://arxiv.org/pdf/1312.5446.pdf#page={{{2|1}}}'''Planck intermediate results. XVII. Emission of dust in the diffuse interstellar medium from the far-infrared to microwave frequencies'''], Planck Collaboration Int. XVII, A&A, in press, (2014).<br />
|planck2011-1-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16464-11.pdf#page={{{2|1}}}'''Planck early results. I. The Planck mission'''], Planck Collaboration I, A&A, '''536''', A1, (2011).<br />
|planck2011-1-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16486-11.pdf#page={{{2|1}}}'''Planck early results. II. The thermal performance of Planck'''], Planck Collaboration II, A&A, '''536''', A2, (2011).<br />
|planck2011-1-4=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16480-11.pdf#page={{{2|1}}}'''Planck early results. III. First assessment of the Low Frequency Instrument in-flight performance'''], A. Mennella, R. C. Butler, A. Curto, F. Cuttaia, R. J. Davis, J. Dick, M. Frailis, S. Galeotta, A. Gregorio, H. Kurki-Suonio, C. R. Lawrence, S. Leach, J. P. Leahy, S. Lowe, D. Maino, N. Mandolesi, M. Maris, E. Mart&iacute;nez-Gonz&aacute;lez, P. R. Meinhold, G. Morgante, D. Pearson, F. Perrotta, G. Polenta, T. Poutanen, M. Sandri, M. D. Seiffert, A.-S. Suur-Uski, D. Tavagnacco, L. Terenzi, M. Tomasi, J. Valiviita, F. Villa, R. Watson, A. Wilkinson, A. Zacchei, A. Zonca, B. Aja, E. Artal, C. Baccigalupi, A. J. Banday, R. B. Barreiro, J. G. Bartlett, N. Bartolo, P. Battaglia, K. Bennett, A. Bonaldi, L. Bonavera, J. Borrill, F. R. Bouchet, C. Burigana, P. Cabella, B. Cappellini, X. Chen, L. Colombo, M. Cruz, L. Danese, O. D'Arcangelo, R. D. Davies, G. de Gasperis, A. de Rosa, G. de Zotti, C. Dickinson, J. M. Diego, S. Donzelli, G. Efstathiou, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, S. Foley, C. Franceschet, E. Franceschi, T. C. Gaier, R. T. G&eacute;nova-Santos, D. George, F. G&oacute;mez, J. Gonz&aacute;lez-Nuevo, K. M. G&oacute;rski, A. Gruppuso, F. K. Hansen, D. Herranz, J. M. Herreros, R. J. Hoyland, N. Hughes, J. Jewell, P. Jukkala, M. Juvela, P. Kangaslahti, E. Keih&auml;nen, R. Keskitalo, V.-H. Kilpia, T. S. Kisner, J. Knoche, L. Knox, M. Laaninen, A. L&auml;hteenm&auml;ki, J.-M. Lamarre, R. Leonardi, J. Le&oacute;n-Tavares, P. Leutenegger, P. B. Lilje, M. L&oacute;pez-Caniego, P. M. Lubin, M. Malaspina, D. Marinucci, M. Massardi, S. Matarrese, F. Matthai, A. Melchiorri, L. Mendes, M. Miccolis, M. Migliaccio, S. Mitra, A. Moss, P. Natoli, R. Nesti, H. U. N&oslash;rgaard-Nielsen, L. Pagano, R. Paladini, D. Paoletti, B. Partridge, F. Pasian, V. Pettorino, D. Pietrobon, M. Pospieszalski, G. Pr&eacute;zeau, M. Prina, P. Procopio, J.-L. Puget, C. Quercellini, J. P. Rachen, R. Rebolo, M. Reinecke, S. Ricciardi, G. Robbers, G. Rocha, N. Roddis, J. A. Rubi&ntilde;o-Mart&iacute;n, M. Savelainen, D. Scott, R. Silvestri, A. Simonetto, P. Sjoman, G. F. Smoot, C. Sozzi, L. Stringhetti, J. A. Tauber, G. Tofani, L. Toffolatti, J. Tuovinen, M. T&uuml;rler, G. Umana, L. Valenziano, J. Varis, P. Vielva, N. Vittorio, L. A. Wade, C. Watson, S. D. M. White, F. Winder, A&A, '''536''', A3, (2011).<br />
|planck2011-1-5=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16487-11.pdf#page={{{2|1}}}'''Planck early results, IV. First assessment of the High Frequency Instrument in-flight performance'''], Planck HFI Core Team, A&A, '''536''', A4, (2011).<br />
|planck2011-1-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16484-11.pdf#page={{{2|1}}}'''Planck early results. V. The Low Frequency Instrument data processing'''], A. Zacchei, D. Maino, C. Baccigalupi, M. Bersanelli, A. Bonaldi, L. Bonavera, C. Burigana, R. C. Butler, F. Cuttaia, G. de Zotti, J. Dick, M. Frailis, S. Galeotta, J. Gonz&aacute;lez-Nuevo, K. M. G&oacute;rski, A. Gregorio, E. Keih&auml;nen, R. Keskitalo, J. Knoche, H. Kurki-Suonio, C. R. Lawrence, S. Leach, J. P. Leahy, M. L&oacute;pez-Caniego, N. Mandolesi, M. Maris, F. Matthai, P. R. Meinhold, A. Mennella, G. Morgante, N. Morisset, P. Natoli, F. Pasian, F. Perrotta, G. Polenta, T. Poutanen, M. Reinecke, S. Ricciardi, R. Rohlfs, M. Sandri, A.-S. Suur-Uski, J. A. Tauber, D. Tavagnacco, L. Terenzi, M. Tomasi, J. Valiviita, F. Villa, A. Zonca, A. J. Banday, R. B. Barreiro, J. G. Bartlett, N. Bartolo, L. Bedini, K. Bennett, P. Binko, J. Borrill, F. R. Bouchet, M. Bremer, P. Cabella, B. Cappellini, X. Chen, L. Colombo, M. Cruz, A. Curto, L. Danese, R. D. Davies, R. J. Davis, G. de Gasperis, A. de Rosa, G. de Troia, C. Dickinson, J. M. Diego, S. Donzelli, U. D&ouml;rl, G. Efstathiou, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, E. Franceschi, T. C. Gaier, F. Gasparo, R. T. G&eacute;nova-Santos, G. Giardino, F. G&oacute;mez, A. Gruppuso, F. K. Hansen, R. Hell, D. Herranz, W. Hovest, M. Huynh, J. Jewell, M. Juvela, T. S. Kisner, L. Knox, A. L&auml;hteenm&auml;ki, J.-M. Lamarre, R. Leonardi, J. Le&oacute;n-Tavares, P. B. Lilje, P. M. Lubin, G. Maggio, D. Marinucci, E. Mart&iacute;nez-Gonz&aacute;lez, M. Massardi, S. Matarrese, M. T. Meharga, A. Melchiorri, M. Migliaccio, S. Mitra, A. Moss, H. U. N&oslash;rgaard-Nielsen, L. Pagano, R. Paladini, D. Paoletti, B. Partridge, D. Pearson, V. Pettorino, D. Pietrobon, G. Pr&eacute;zeau, P. Procopio, J.-L. Puget, C. Quercellini, J. P. Rachen, R. Rebolo, G. Robbers, G. Rocha, J. A. Rubi&ntilde;o-Mart&iacute;n, E. Salerno, M. Savelainen, D. Scott, M. D. Seiffert, J. I. Silk, G. F. Smoot, J. Sternberg, F. Stivoli, R. Stompor, G. Tofani, L. Toffolatti, J. Tuovinen, M. T&uuml;rler, G. Umana, P. Vielva, N. Vittorio, C. Vuerli, L. A. Wade, R. Watson, S. D. M. White, A. Wilkinson, A&A, '''536''', A5, (2011).<br />
|planck2011-1-7=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16462-11.pdf#page={{{2|1}}}'''Planck early results. VI. The High Frequency Instrument data processing'''], Planck HFI Core Team, A&A, '''536''', A6, (2011).<br />
|planck2011-1-10=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16474-11.pdf#page={{{2|1}}}'''Planck early results. VII. The Early Release Compact Source Catalogue'''], Planck Collaboration VII, A&A, '''536''', A7, (2011).<br />
|planck2011-1-10sup=[http://arxiv.org '''The Explanatory Supplement to the Planck Early Release Compact Source Catalogue'''], Planck Collaboration, ESA, (2011).<br />
|planck2011-5-1a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16459-11.pdf#page={{{2|1}}}'''Planck early results. VIII. The all-sky early Sunyaev-Zeldovich cluster sample'''], Planck Collaboration VIII, A&A, '''536''', A8, (2011).<br />
|planck2011-5-1b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16460-11.pdf#page={{{2|1}}}'''Planck early results. IX. XMM-Newton follow-up for validation of Planck cluster candidates'''], Planck Collaboration IX, A&A, '''536''', A9, (2011).<br />
|planck2011-5-2a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16457-11.pdf#page={{{2|1}}}'''Planck early results. X. Statistical analysis of Sunyaev-Zeldovich scaling relations for X-ray galaxy clusters'''], Planck Collaboration X, A&A, '''536''', A10, (2011).<br />
|planck2011-5-2b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16458-11.pdf#page={{{2|1}}}'''Planck early results. XI. Calibration of the local galaxy cluster Sunyaev-Zeldovich scaling relations'''], Planck Collaboration XI, A&A, '''536''', A11, (2011).<br />
|planck2011-5-2c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16489-11.pdf#page={{{2|1}}}'''Planck early results. XII. Cluster Sunyaev-Zeldovich optical Scaling relations'''], Planck Collaboration XII, A&A, '''536''', A12, (2011).<br />
|planck2011-6-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16471-11.pdf#page={{{2|1}}}'''Planck early results. XIII. Statistical properties of extragalactic radio sources in the Planck Early Release Compact Source Catalogue'''], Planck Collaboration XIII, A&A, '''536''', A13, (2011).<br />
|planck2011-6-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16475-11.pdf#page={{{2|1}}}'''Planck early results. XIV. Early Release Compact Source Catalogue validation and extreme radio sources'''], Planck Collaboration XIV, A&A, '''536''', A14, (2011).<br />
|planck2011-6-3a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16466-11.pdf#page={{{2|1}}}'''Planck early results. XV. Spectral energy distributions and radio continuum spectra of northern extragalactic radio sources'''], Planck Collaboration XV, A&A, '''536''', A15, (2011).<br />
|planck2011-6-4a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16454-11.pdf#page={{{2|1}}}'''Planck early results. XVI. The Planck view of nearby galaxies'''], Planck Collaboration XVI, A&A, '''536''', A16, (2011).<br />
|planck2011-6-4b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16473-11.pdf#page={{{2|1}}}'''Planck early results. XVII. Origin of the submillimetre excess dust emission in the Magellanic Clouds'''], Planck Collaboration XVII, A&A, '''536''', A17, (2011).<br />
|planck2011-6-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16461-11.pdf#page={{{2|1}}}'''Planck early results. XVIII. The power spectrum of cosmic infrared background anisotropies'''], Planck Collaboration XVIII, A&A, '''536''', A18, (2011).<br />
|planck2011-7-0=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16479-11.pdf#page={{{2|1}}}'''Planck early results. XIX. All-sky temperature and dust optical <br />
depth from Planck and IRAS. Constraints on the dark gas in our Galaxy'''], Planck Collaboration XIX, A&A, '''536''', A19, (2011).<br />
|planck2011-7-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16470-11.pdf#page={{{2|1}}}'''Planck early results. XX. New light on anomalous microwave emission from spinning dust grains'''], Planck Collaboration XX, A&A, '''536''', A20, (2011).<br />
|planck2011-7-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16455-11.pdf#page={{{2|1}}}'''Planck early results. XXI. Properties of the interstellar medium in the Galactic plane'''], Planck Collaboration XXI, A&A, '''536''', A21, (2011).<br />
|planck2011-7-7a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16481-11.pdf#page={{{2|1}}}'''Planck early results. XXII. The submillimetre properties of a sample of Galactic cold clumps'''], Planck Collaboration XXII, A&A, '''536''', A22, (2011).<br />
|planck2011-7-7b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16472-11.pdf#page={{{2|1}}}'''Planck early results. XXIII. The Galactic cold core population revealed by the first all-sky survey'''], Planck Collaboration XXIII, A&A, '''536''', A23, (2011).<br />
|planck2011-7-12=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16485-11.pdf#page={{{2|1}}}'''Planck early results. XXIV. Dust in the diffuse interstellar medium and the Galactic halo'''], Planck Collaboration XXIV, A&A, '''536''', A24, (2011).<br />
|planck2011-7-13=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16483-11.pdf#page={{{2|1}}}'''Planck early results. XXV. Thermal dust in nearby molecular clouds'''], Planck Collaboration XXV, A&A, '''536''', A25, (2011).<br />
|planck2011-5-1c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa17430-11.pdf#page={{{2|1}}}'''Planck early results. XXVI. Detection with Planck and confirmation by XMM-Newton of PLCK G266.6-27.3, an exceptionally X-ray luminous and massive galaxy cluster at z 1'''], Planck Collaboration XXVI, A&A, '''536''', A26, (2011).<br />
|planck2011-6-3b=[http://www.aanda.org/articles/aa/pdf/2012/05/aa17825-11.pdf#page={{{2|1}}}'''Simultaneous Planck, Swift, and Fermi observations of X-ray and $\gamma$-ray selected blazars'''], P. Giommi, G. Polenta, A. L&auml;hteenm&auml;ki, D. J. Thompson, M. Capalbi, S. Cutini, D. Gasparrini, J. Gonz&aacute;lez-Nuevo, J. Le&oacute;n-Tavares, M. L&oacute;pez-Caniego, M. N. Mazziotta, C. Monte, M. Perri, S. Rain&ograve;, G. Tosti, A. Tramacere, F. Verrecchia, H. D. Aller, M. F. Aller, E. Angelakis, D. Bastieri, A. Berdyugin, A. Bonaldi, L. Bonavera, C. Burigana, D. N. Burrows, S. Buson, E. Cavazzuti, G. Chincarini, S. Colafrancesco, L. Costamante, F. Cuttaia, F. D'Ammando, G. de Zotti, M. Frailis, L. Fuhrmann, S. Galeotta, F. Gargano, N. Gehrels, N. Giglietto, F. Giordano, M. Giroletti, E. Keih&auml;nen, O. King, T. P. Krichbaum, A. Lasenby, N. Lavonen, C. R. Lawrence, C. Leto, E. Lindfors, N. Mandolesi, M. Massardi, W. Max-Moerbeck, P. F. Michelson, M. Mingaliev, P. Natoli, I. Nestoras, E. Nieppola, K. Nilsson, B. Partridge, V. Pavlidou, T. J. Pearson, P. Procopio, J. P. Rachen, A. Readhead, R. Reeves, A. Reimer, R. Reinthal, S. Ricciardi, J. Richards, D. Riquelme, J. Saarinen, A. Sajina, M. Sandri, P. Savolainen, A. Sievers, A. Sillanp&auml;&auml;, Y. Sotnikova, M. Stevenson, G. Tagliaferri, L. Takalo, J. Tammi, D. Tavagnacco, L. Terenzi, L. Toffolatti, M. Tornikoski, C. Trigilio, M. Turunen, G. Umana, H. Ungerechts, F. Villa, J. Wu, A. Zacchei, J. A. Zensus, X. Zhou, A&A, '''541''', A160, (2012).<br />
|ade2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13039-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The optical architecture of the HFI'''], P. A. R. Ade, G. Savini, R. Sudiwala, C. Tucker, A. Catalano, S. Church, R. Colgan, F. X. Desert, E. Gleeson, W. C. Jones, J.-M. Lamarre, A. Lange, Y. Longval, B. Maffei, J. A. Murphy, F. Noviello, F. Pajot, J.-L. Puget, I. Ristorcelli, A. Woodcraft, V. Yurchenko, A&A, '''520''', A11+, (2010).<br />
|bersanelli2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12853-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Design and description of the Low Frequency Instrument'''], M. Bersanelli, N. Mandolesi, R. C. Butler, A. Mennella, F. Villa, B. Aja, E. Artal, E. Artina, C. Baccigalupi, M. Balasini, G. Baldan, A. Banday, P. Bastia, P. Battaglia, T. Bernardino, E. Blackhurst, L. Boschini, C. Burigana, G. Cafagna, B. Cappellini, F. Cavaliere, F. Colombo, G. Crone, F. Cuttaia, O. D'Arcangelo, L. Danese, R. D. Davies, R. J. Davis, L. de Angelis, G. C. de Gasperis, L. de La Fuente, A. de Rosa, G. de Zotti, M. C. Falvella, F. Ferrari, R. Ferretti, L. Figini, S. Fogliani, C. Franceschet, E. Franceschi, T. Gaier, S. Garavaglia, F. Gomez, K. Gorski, A. Gregorio, P. Guzzi, J. M. Herreros, S. R. Hildebrandt, R. Hoyland, N. Hughes, M. Janssen, P. Jukkala, D. Kettle, V. H. Kilpi&auml;, M. Laaninen, P. M. Lapolla, C. R. Lawrence, D. Lawson, J. P. Leahy, R. Leonardi, P. Leutenegger, S. Levin, P. B. Lilje, S. R. Lowe, P. M. Lubin, D. Maino, M. Malaspina, M. Maris, J. Marti-Canales, E. Martinez-Gonzalez, A. Mediavilla, P. Meinhold, M. Miccolis, G. Morgante, P. Natoli, R. Nesti, L. Pagan, C. Paine, B. Partridge, J. P. Pascual, F. Pasian, D. Pearson, M. Pecora, F. Perrotta, P. Platania, M. Pospieszalski, T. Poutanen, M. Prina, R. Rebolo, N. Roddis, J. A. Rubi&ntilde;o-Martin, M. J. Salmon, M. Sandri, M. Seiffert, R. Silvestri, A. Simonetto, P. Sjoman, G. F. Smoot, C. Sozzi, L. Stringhetti, E. Taddei, J. Tauber, L. Terenzi, M. Tomasi, J. Tuovinen, L. Valenziano, J. Varis, N. Vittorio, L. A. Wade, A. Wilkinson, F. Winder, A. Zacchei, A. Zonca, A&A, '''520''', A4+, (2010).<br />
|lamarre2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12975-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The HFI instrument, from specification to actual performance'''], J.-M. Lamarre, J.-L. Puget, P. A. R. Ade, F. Bouchet, G. Guyot, A. E. Lange, F. Pajot, A. Arondel, K. Benabed, J.-L. Beney, A. Beno&icirc;t, J.-P. Bernard, R. Bhatia, Y. Blanc, J. J. Bock, E. Br&eacute;elle, T. W. Bradshaw, P. Camus, A. Catalano, J. Charra, M. Charra, S. E. Church, F. Couchot, A. Coulais, B. P. Crill, M. R. Crook, K. Dassas, P. de Bernardis, J. Delabrouille, P. de Marcillac, J.-M. Delouis, F.-X. D&eacute;sert, C. Dumesnil, X. Dupac, G. Efstathiou, P. Eng, C. Evesque, J.-J. Fourmond, K. Ganga, M. Giard, R. Gispert, L. Guglielmi, J. Haissinski, S. Henrot-Versill&eacute;, E. Hivon, W. A. Holmes, W. C. Jones, T. C. Koch, H. Lagard&egrave;re, P. Lami, J. Land&eacute;, B. Leriche, C. Leroy, Y. Longval, J. F. Mac&iacute;as-P&eacute;rez, T. Maciaszek, B. Maffei, B. Mansoux, C. Marty, S. Masi, C. Mercier, M.-A. Miville-Desch&ecirc;nes, A. Moneti, L. Montier, J. A. Murphy, J. Narbonne, M. Nexon, C. G. Paine, J. Pahn, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, R. Pons, N. Ponthieu, S. Prunet, D. Rambaud, G. Recouvreur, C. Renault, I. Ristorcelli, C. Rosset, D. Santos, G. Savini, G. Serra, P. Stassi, R. V. Sudiwala, J.-F. Sygnet, J. A. Tauber, J.-P. Torre, M. Tristram, L. Vibert, A. Woodcraft, V. Yurchenko, D. Yvon, A&A, '''520''', A9+, (2010).<br />
|leahy2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12855-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Expected LFI polarisation capability'''], J. P. Leahy, M. Bersanelli, O. D'Arcangelo, K. Ganga, S. M. Leach, A. Moss, E. Keih&auml;nen, R. Keskitalo, H. Kurki-Suonio, T. Poutanen, M. Sandri, D. Scott, J. Tauber, L. Valenziano, F. Villa, A. Wilkinson, A. Zonca, C. Baccigalupi, J. Borrill, R. C. Butler, F. Cuttaia, R. J. Davis, M. Frailis, E. Francheschi, S. Galeotta, A. Gregorio, R. Leonardi, N. Mandolesi, M. Maris, P. Meinhold, L. Mendes, A. Mennella, G. Morgante, G. Prezeau, G. Rocha, L. Stringhetti, L. Terenzi, M. Tomasi, A&A, '''520''', A8+, (2010).<br />
|maffei2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12999-09.pdf#page={{{2|1}}}'''Planck pre-launch status: HFI beam expectations from the optical optimisation of the focal plane'''], B. Maffei, F. Noviello, J. A. Murphy, P. A. R. Ade, J.-M. Lamarre, F. R. Bouchet, J. Brossard, A. Catalano, R. Colgan, R. Gispert, E. Gleeson, C. V. Haynes, W. C. Jones, A. E. Lange, Y. Longval, I. McAuley, F. Pajot, T. Peacocke, G. Pisano, J.-L. Puget, I. Ristorcelli, G. Savini, R. Sudiwala, R. J. Wylde, V. Yurchenko, A&A, '''520''', A12+, (2010).<br />
|mandolesi2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12837-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The Planck-LFI programme'''], N. Mandolesi, M. Bersanelli, R. C. Butler, E. Artal, C. Baccigalupi, A. Balbi, A. J. Banday, R. B. Barreiro, M. Bartelmann, K. Bennett, P. Bhandari, A. Bonaldi, J. Borrill, M. Bremer, C. Burigana, R. C. Bowman, P. Cabella, C. Cantalupo, B. Cappellini, T. Courvoisier, G. Crone, F. Cuttaia, L. Danese, O. D'Arcangelo, R. D. Davies, R. J. Davis, L. de Angelis, G. de Gasperis, A. de Rosa, G. de Troia, G. de Zotti, J. Dick, C. Dickinson, J. M. Diego, S. Donzelli, U. D&ouml;rl, X. Dupac, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, M. Frailis, E. Franceschi, T. Gaier, S. Galeotta, F. Gasparo, G. Giardino, F. Gomez, J. Gonzalez-Nuevo, K. M. G&oacute;rski, A. Gregorio, A. Gruppuso, F. Hansen, R. Hell, D. Herranz, J. M. Herreros, S. Hildebrandt, W. Hovest, R. Hoyland, K. Huffenberger, M. Janssen, T. Jaffe, E. Keih&auml;nen, R. Keskitalo, T. Kisner, H. Kurki-Suonio, A. L&auml;hteenm&auml;ki, C. R. Lawrence, S. M. Leach, J. P. Leahy, R. Leonardi, S. Levin, P. B. Lilje, M. L&oacute;pez-Caniego, S. R. Lowe, P. M. Lubin, D. Maino, M. Malaspina, M. Maris, J. Marti-Canales, E. Martinez-Gonzalez, M. Massardi, S. Matarrese, F. Matthai, P. Meinhold, A. Melchiorri, L. Mendes, A. Mennella, G. Morgante, G. Morigi, N. Morisset, A. Moss, A. Nash, P. Natoli, R. Nesti, C. Paine, B. Partridge, F. Pasian, T. Passvogel, D. Pearson, L. P&eacute;rez-Cuevas, F. Perrotta, G. Polenta, L. A. Popa, T. Poutanen, G. Prezeau, M. Prina, J. P. Rachen, R. Rebolo, M. Reinecke, S. Ricciardi, T. Riller, G. Rocha, N. Roddis, R. Rohlfs, J. A. Rubi&ntilde;o-Martin, E. Salerno, M. Sandri, D. Scott, M. Seiffert, J. Silk, A. Simonetto, G. F. Smoot, C. Sozzi, J. Sternberg, F. Stivoli, L. Stringhetti, J. Tauber, L. Terenzi, M. Tomasi, J. Tuovinen, M. T&uuml;rler, L. Valenziano, J. Varis, P. Vielva, F. Villa, N. Vittorio, L. Wade, M. White, S. White, A. Wilkinson, A. Zacchei, A. Zonca, A&A, '''520''', A3+, (2010).<br />
|mennella2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12849-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Low Frequency Instrument calibration and expected scientific performance'''], A. Mennella, M. Bersanelli, R. C. Butler, F. Cuttaia, O. D'Arcangelo, R. J. Davis, M. Frailis, S. Galeotta, A. Gregorio, C. R. Lawrence, R. Leonardi, S. R. Lowe, N. Mandolesi, M. Maris, P. Meinhold, L. Mendes, G. Morgante, M. Sandri, L. Stringhetti, L. Terenzi, M. Tomasi, L. Valenziano, F. Villa, A. Zacchei, A. Zonca, M. Balasini, C. Franceschet, P. Battaglia, P. M. Lapolla, P. Leutenegger, M. Miccolis, L. Pagan, R. Silvestri, B. Aja, E. Artal, G. Baldan, P. Bastia, T. Bernardino, L. Boschini, G. Cafagna, B. Cappellini, F. Cavaliere, F. Colombo, L. de La Fuente, J. Edgeley, M. C. Falvella, F. Ferrari, S. Fogliani, E. Franceschi, T. Gaier, F. Gomez, J. M. Herreros, S. Hildebrandt, R. Hoyland, N. Hughes, P. Jukkala, D. Kettle, M. Laaninen, D. Lawson, P. Leahy, S. Levin, P. B. Lilje, D. Maino, M. Malaspina, P. Manzato, J. Marti-Canales, E. Martinez-Gonzalez, A. Mediavilla, F. Pasian, J. P. Pascual, M. Pecora, L. Peres-Cuevas, P. Platania, M. Pospieszalsky, T. Poutanen, R. Rebolo, N. Roddis, M. Salmon, M. Seiffert, A. Simonetto, C. Sozzi, J. Tauber, J. Tuovinen, J. Varis, A. Wilkinson, F. Winder, A&A, '''520''', A5+, (2010).<br />
|pajot2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13203-09.pdf#page={{{2|1}}}'''Planck pre-launch status: HFI ground calibration'''], F. Pajot, P. A. R. Ade, J.-L. Beney, E. Br&eacute;elle, D. Broszkiewicz, P. Camus, C. Carab&eacute;tian, A. Catalano, A. Chardin, M. Charra, J. Charra, R. Cizeron, F. Couchot, A. Coulais, B. P. Crill, K. Dassas, J. Daubin, P. de Bernardis, P. de Marcillac, J.-M. Delouis, F.-X. D&eacute;sert, P. Duret, P. Eng, C. Evesque, J.-J. Fourmond, S. Fran&ccedel;ois, M. Giard, Y. Giraud-H&eacute;raud, L. Guglielmi, G. Guyot, J. Haissinski, S. Henrot-Versill&eacute;, V. Hervier, W. Holmes, W. C. Jones, J.-M. Lamarre, P. Lami, A. E. Lange, M. Lefebvre, B. Leriche, C. Leroy, J. Macias-Perez, T. Maciaszek, B. Maffei, A. Mahendran, B. Mansoux, C. Marty, S. Masi, C. Mercier, M.-A. Miville-Deschenes, L. Montier, C. Nicolas, F. Noviello, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, R. Pons, N. Ponthieu, J.-L. Puget, D. Rambaud, C. Renault, J.-C. Renault, C. Rioux, I. Ristorcelli, C. Rosset, G. Savini, R. Sudiwala, J.-P. Torre, M. Tristram, D. Vall&eacute;e, M. Veneziani, D. Yvon, A&A, '''520''', A10+, (2010).<br />
|rosset2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13054-09.pdf#page={{{2|1}}}'''Planck pre-launch status: High Frequency Instrument polarization calibration'''], C. Rosset, M. Tristram, N. Ponthieu, P. Ade, J. Aumont, A. Catalano, L. Conversi, F. Couchot, B. P. Crill, F.-X. D&eacute;sert, K. Ganga, M. Giard, Y. Giraud-H&eacute;raud, J. Ha&iuml;ssinski, S. Henrot-Versill&eacute;, W. Holmes, W. C. Jones, J.-M. Lamarre, A. Lange, C. Leroy, J. Mac&iacute;as-P&eacute;rez, B. Maffei, P. de Marcillac, M.-A. Miville-Desch&ecirc;nes, L. Montier, F. Noviello, F. Pajot, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, J.-L. Puget, I. Ristorcelli, G. Savini, R. Sudiwala, M. Veneziani, D. Yvon, A&A, '''520''', A13+, (2010).<br />
|sandri2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12891-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Low Frequency Instrument optics'''], M. Sandri, F. Villa, M. Bersanelli, C. Burigana, R. C. Butler, O. D'Arcangelo, L. Figini, A. Gregorio, C. R. Lawrence, D. Maino, N. Mandolesi, M. Maris, R. Nesti, F. Perrotta, P. Platania, A. Simonetto, C. Sozzi, J. Tauber, L. Valenziano, A&A, '''520''', A7+, (2010).<br />
|tauber2010a=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12983-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The Planck mission'''], J. A. Tauber, N. Mandolesi, J.-L. Puget, T. Banos, M. Bersanelli, F. R. Bouchet, R. C. Butler, J. Charra, G. Crone, J. Dodsworth, et al., A&A, '''520''', A1+, (2010).<br />
|tauber2010b=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12911-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The optical system'''], J. A. Tauber, H. U. N&oslash;rgaard-Nielsen, P. A. R. Ade, J. Amiri Parian, T. Banos, M. Bersanelli, C. Burigana, A. Chamballu, D. de Chambure, P. R. Christensen, O. Corre, A. Cozzani, B. Crill, G. Crone, O. D'Arcangelo, R. Daddato, D. Doyle, D. Dubruel, G. Forma, R. Hills, K. Huffenberger, A. H. Jaffe, N. Jessen, P. Kletzkine, J. M. Lamarre, J. P. Leahy, Y. Longval, P. de Maagt, B. Maffei, N. Mandolesi, J. Mart&iacute;-Canales, A. Mart&iacute;n-Polegre, P. Martin, L. Mendes, J. A. Murphy, P. Nielsen, F. Noviello, M. Paquay, T. Peacocke, N. Ponthieu, K. Pontoppidan, I. Ristorcelli, J.-B. Riti, L. Rolo, C. Rosset, M. Sandri, G. Savini, R. Sudiwala, M. Tristram, L. Valenziano, M. van der Vorst, K. van't Klooster, F. Villa, V. Yurchenko, A&A, '''520''', A2+, (2010).<br />
|villa2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12860-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Calibration of the Low Frequency Instrument flight model radiometers'''], F. Villa, L. Terenzi, M. Sandri, P. Meinhold, T. Poutanen, P. Battaglia, C. Franceschet, N. Hughes, M. Laaninen, P. Lapolla, M. Bersanelli, R. C. Butler, F. Cuttaia, O. D'Arcangelo, M. Frailis, E. Franceschi, S. Galeotta, A. Gregorio, R. Leonardi, S. R. Lowe, N. Mandolesi, M. Maris, L. Mendes, A. Mennella, G. Morgante, L. Stringhetti, M. Tomasi, L. Valenziano, A. Zacchei, A. Zonca, B. Aja, E. Artal, M. Balasini, T. Bernardino, E. Blackhurst, L. Boschini, B. Cappellini, F. Cavaliere, A. Colin, F. Colombo, R. J. Davis, L. de La Fuente, J. Edgeley, T. Gaier, A. Galtress, R. Hoyland, P. Jukkala, D. Kettle, V.-H. Kilpia, C. R. Lawrence, D. Lawson, J. P. Leahy, P. Leutenegger, S. Levin, D. Maino, M. Malaspina, A. Mediavilla, M. Miccolis, L. Pagan, J. P. Pascual, F. Pasian, M. Pecora, M. Pospieszalski, N. Roddis, M. J. Salmon, M. Seiffert, R. Silvestri, A. Simonetto, P. Sjoman, C. Sozzi, J. Tuovinen, J. Varis, A. Wilkinson, F. Winder, A&A, '''520''', A6+, (2010).<br />
}}<br />
|name={{{1}}}}}</includeonly></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Template:PlanckPapers&diff=11612Template:PlanckPapers2017-12-04T19:55:16Z<p>Mlopezca: </p>
<hr />
<div><noinclude><br />
==Description==<br />
The template <tt>PlanckPapers</tt> produces references for Planck papers in the text. There are three differences between this template and the [[Template:BibCite|BibCite]].<br />
*<tt>PlanckPapers</tt> creates in the text a direct link to the papers being cited;<br />
*With <tt>PlanckPapers</tt>, it is possible to create a link to a specific page of the paper;<br />
*With <tt>PlanckPapers</tt> it is possible to create a link with text defined by the user;<br />
The syntax of <tt>PlanckPapers</tt> is<br />
<pre><br />
{{PlanckPapers|label|page|link}}<br />
</pre><br />
where the thre parameters are as follows:<br />
;<tt>label</tt><br />
:label used to cite the paper in LaTeX;<br />
;<tt>page</tt><br />
:(optional parameter) page number where the paper paper should open when the user clicks on the link. When this parameter is not provided the paperwill open in page 1;<br />
;<tt>link</tt><br />
:(optional parameter) text of the link to the paper. If not supplied the default text is one of '''Planck-2013-'''X, '''Planck-Early-'''X or '''Planck-Int-'''X or '''Planck-PreLaunch-'''X, for the Planck 2013 papers, Planck early papers, Planck internediate papers or Planck pre launch papers. The X stands for a roman numeral identifying the paper being cited;<br />
<br />
==Examples==<br />
===Citing a paper using all the default values for optional parameters===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2013-p01}}<br />
</pre><br />
'''How it appears'''<br />
<br />
A very interesting result can be found in {{PlanckPapers|planck2013-p01}}.<br />
<br />
===Citing a paper and opening it in a given page===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2014-a01|10}}<br />
</pre><br />
'''How it appears'''<br />
<br />
On the other hand in {{PlanckPapers|planck2014-a01|10}} a demonstration is given of a most interesting fact.<br />
<br />
In this case the paper opens in page 10.<br />
<br />
===Citing a paper with a custom link===<br />
'''What you type'''<br />
<pre><br />
{{PlanckPapers|planck2013-p09||isotropy and statistics paper}}<br />
</pre><br />
'''How it appears'''<br />
<br />
On the other hand it is shown in the {{PlanckPapers|planck2013-p09||isotropy and statistics paper}} the above mentioned result is rubish.<br />
<br />
Note that in this last case, the second argument (the page number where the paper should open) does not need to have a value and it is perfectly fine for the corresponding slot in the template syntax (delimited by the <tt>||</tt> in the template syntax) to be emtpy. It is however mandatory to use the two <tt>|</tt> in order for the template to be interpreted correctly.<br />
<br />
===Displaying the list of references at the end of the page===<br />
Have a look at [[Template:BibCite#Displaying_the_list_of_references_at_the_bottom_of_a_page|BibCite]].<br />
<br />
= References =<br />
<References /><br />
</noinclude><includeonly>{{#switch: {{{1}}}<br />
|planck2014-a01=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27101-15/aa27101-15.html#page={{{2|1}}} {{{3|Planck-2015-A01}}}]<br />
|planck2014-a03=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25818-15/aa25818-15.html#page={{{2|1}}} {{{3|Planck-2015-A02}}}]<br />
|planck2014-a04=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26998-15/aa26998-15.html#page={{{2|1}}} {{{3|Planck-2015-A03}}}]<br />
|planck2014-a05=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25809-15/aa25809-15.html#page={{{2|1}}} {{{3|Planck-2015-A04}}}]<br />
|planck2014-a06=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26632-15/aa26632-15.html#page={{{2|1}}} {{{3|Planck-2015-A05}}}]<br />
|planck2014-a07=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25813-15/aa25813-15.html#page={{{2|1}}} {{{3|Planck-2015-A06}}}]<br />
|planck2014-a08=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25844-15/aa25844-15.html#page={{{2|1}}} {{{3|Planck-2015-A07}}}]<br />
|planck2014-a09=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25820-15/aa25820-15.html#page={{{2|1}}} {{{3|Planck-2015-A08}}}]<br />
|planck2014-a11=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25936-15/aa25936-15.html#page={{{2|1}}} {{{3|Planck-2015-A09}}}]<br />
|planck2014-a12=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html#page={{{2|1}}} {{{3|Planck-2015-A10}}}]<br />
|planck2014-a13=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26926-15/aa26926-15.html#page={{{2|1}}} {{{3|Planck-2015-A11}}}]<br />
|planck2014-a14=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27103-15/aa27103-15.html#page={{{2|1}}} {{{3|Planck-2015-A12}}}]<br />
|planck2014-a15=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25830-15/aa25830-15.html#page={{{2|1}}} {{{3|Planck-2015-A13}}}]<br />
|planck2014-a16=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25814-15/aa25814-15.html#page={{{2|1}}} {{{3|Planck-2015-A14}}}]<br />
|planck2014-a17=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25941-15/aa25941-15.html#page={{{2|1}}} {{{3|Planck-2015-A15}}}]<br />
|planck2014-a18=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26681-15/aa26681-15.html#page={{{2|1}}} {{{3|Planck-2015-A16}}}]<br />
|planck2014-a19=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25836-15/aa25836-15.html#page={{{2|1}}} {{{3|Planck-2015-A17}}}]<br />
|planck2014-a20=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25829-15/aa25829-15.html#page={{{2|1}}} {{{3|Planck-2015-A18}}}]<br />
|planck2014-a22=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25821-15/aa25821-15.html#page={{{2|1}}} {{{3|Planck-2015-A19}}}]<br />
|planck2014-a24=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25898-15/aa25898-15.html#page={{{2|1}}} {{{3|Planck-2015-A20}}}]<br />
|planck2014-a26=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25831-15/aa25831-15.html#page={{{2|1}}} {{{3|Planck-2015-A21}}}]<br />
|planck2014-a28=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25826-15/aa25826-15.html#page={{{2|1}}} {{{3|Planck-2015-A22}}}]<br />
|planck2014-a29=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27418-15/aa27418-15.html#page={{{2|1}}} {{{3|Planck-2015-A23}}}]<br />
|planck2014-a30=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25833-15/aa25833-15.html#page={{{2|1}}} {{{3|Planck-2015-A24}}}]<br />
|planck2014-a31=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26803-15/aa26803-15.html#page={{{2|1}}} {{{3|Planck-2015-A25}}}]<br />
|planck2014-a33=[http://arxiv.org#page={{{2|1}}} {{{3|Planck-2015-A33}}}]<br />
|planck2014-a35=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26914-15/aa26914-15.html#page={{{2|1}}} {{{3|Planck-2015-A26}}}]<br />
|planck2014-a36=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25823-15/aa25823-15.html#page={{{2|1}}} {{{3|Planck-2015-A27}}}]<br />
|planck2014-a37=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25819-15/aa25819-15.html#page={{{2|1}}} {{{3|Planck-2015-A28}}}]<br />
|pb2015=[http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.101301#page={{{2|1}}} {{{3|Planck-BICEP}}}]<br />
|planck2014-XVIII=[http://www.aanda.org/articles/aa/pdf/2015/01/aa23836-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XVIII}}}]<br />
|planck2014-XIX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24082-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XIX}}}]<br />
|planck2014-XX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24086-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XX}}}]<br />
|planck2014-XXI=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24087-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXI}}}]<br />
|planck2014-XXII=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24088-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXII}}}]<br />
|planck2014-XXIII=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24434-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIII}}}]<br />
|planck2014-XXIV=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24496-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIV}}}]<br />
|planck2014-XXV=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24643-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXV}}}]<br />
|planck2014-XXVI=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24674-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVI}}}]<br />
|planck2014-XXVII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24790-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVII}}}]<br />
|planck2014-XXVIII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24955-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXVIII}}}]<br />
|planck2014-XXIX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa24945-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXIX}}}]<br />
|planck2014-XXX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25034-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXX}}}]<br />
|planck2014-XXXI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXI}}}]<br />
|planck2014-XXXII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25044-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXII}}}]<br />
|planck2014-XXXIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25305-14.pdf#page={{{2|1}}} {{{3|Planck-2014-XXXIII}}}]<br />
|planck2015-XXXIV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25616-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXIV}}}]<br />
|planck2015-XXXV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25896-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXV}}}]<br />
|planck2015-XXXVI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26345-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVI}}}]<br />
|planck2015-XXXVII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26328-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVII}}}]<br />
|planck2015-XXXVIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26506-15.pdf#page={{{2|1}}} {{{3|Planck-2015-XXXVIII}}}]<br />
|planck2015-XXXIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27206-15/aa27206-15.html#page={{{2|1}}} {{{3|Planck-2015-XXXIX}}}]<br />
|planck2015-XL=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27743-15/aa27743-15.html#page={{{2|1}}} {{{3|Planck-2015-XL}}}]<br />
|planck2015-XLI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27932-15/aa27932-15.html#page={{{2|1}}} {{{3|Planck-2015-XLI}}}]<br />
|planck2016-XLII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28033-15/aa28033-15.html#page={{{2|1}}} {{{3|Planck-2016-XLII}}}]<br />
|planck2016-XLIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28522-16/aa28522-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIII}}}]<br />
|planck2016-XLIV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28636-16/aa28636-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIV}}}]<br />
|planck2016-XLV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27780-15/aa27780-15.html#page={{{2|1}}} {{{3|Planck-2016-XLV}}}]<br />
|planck2016-XLVI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28890-16/aa28890-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVI}}}]<br />
|planck2016-XLVII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28897-16/aa28897-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVII}}}]<br />
|planck2016-XLVIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29022-16/aa29022-16.html#page={{{2|1}}} {{{3|Planck-2016-XLVIII}}}]<br />
|planck2016-XLIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29018-16/aa29018-16.html#page={{{2|1}}} {{{3|Planck-2016-XLIX}}}]<br />
|planck2016-L=[http://xxx.lanl.gov/abs/1606.07335#page={{{2|1}}} {{{3|Planck-2016-L}}}]<br />
|planck2016-LI=[http://xxx.lanl.gov/abs/1608.02487#page={{{2|1}}} {{{3|Planck-2016-LI}}}]<br />
|planck2017-LII=[http://xxx.lanl.gov/abs/1612.07151#page={{{2|1}}} {{{3|Planck-2017-LII}}}]<br />
|planck2013-p01=[http://www.aanda.org/articles/aa/abs/2014/11/aa21529-13/aa21529-13.html#page={{{2|1}}} {{{3|Planck-2013-I}}}]<br />
|planck2013-p02=[http://www.aanda.org/articles/aa/abs/2014/11/aa21550-13/aa21550-13.html#page={{{2|1}}} {{{3|Planck-2013-II}}}] <br />
|planck2013-p02a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21574-13/aa21574-13.html#page={{{2|1}}} {{{3|Planck-2013-III}}}] <br />
|planck2013-p02d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21544-13/aa21544-13.html#page={{{2|1}}} {{{3|Planck-2013-IV}}}]<br />
|planck2013-p02b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21527-13/aa21527-13.html#page={{{2|1}}} {{{3|Planck-2013-V}}}]<br />
|planck2013-p03=[http://www.aanda.org/articles/aa/abs/2014/11/aa21570-13/aa21570-13.html#page={{{2|1}}} {{{3|Planck-2013-VI}}}]<br />
|planck2013-p03c=[http://www.aanda.org/articles/aa/abs/2014/11/aa21535-13/aa21535-13.html#page={{{2|1}}} {{{3|Planck-2013-VII}}}]<br />
|planck2013-p03b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21538-13/aa21538-13.html#page={{{2|1}}} {{{3|Planck-2013-VIII}}}]<br />
|planck2013-p03d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21531-13/aa21531-13.html#page={{{2|1}}} {{{3|Planck-2013-IX}}}]<br />
|planck2013-p03e=[http://www.aanda.org/articles/aa/abs/2014/11/aa21577-13/aa21577-13.html#page={{{2|1}}} {{{3|Planck-2013-X}}}]<br />
|planck2013-p06b=[http://www.aanda.org/articles/aa/abs/2014/11/aa23195-13/aa23195-13.html#page={{{2|1}}} {{{3|Planck-2013-XI}}}]<br />
|planck2013-p06=[http://www.aanda.org/articles/aa/abs/2014/11/aa21580-13/aa21580-13.html#page={{{2|1}}} {{{3|Planck-2013-XII}}}]<br />
|planck2013-p03a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21553-13/aa21553-13.html#page={{{2|1}}} {{{3|Planck-2013-XIII}}}]<br />
|planck2013-pip88=[http://www.aanda.org/articles/aa/abs/2014/11/aa21562-13/aa21562-13.html#page={{{2|1}}} {{{3|Planck-2013-XIV}}}]<br />
|planck2013-p08=[http://www.aanda.org/articles/aa/abs/2014/11/aa21573-13/aa21573-13.html#page={{{2|1}}} {{{3|Planck-2013-XV}}}]<br />
|planck2013-p11=[http://www.aanda.org/articles/aa/abs/2014/11/aa21591-13/aa21591-13.html#page={{{2|1}}} {{{3|Planck-2013-XVI}}}]<br />
|planck2013-p12=[http://www.aanda.org/articles/aa/abs/2014/11/aa21543-13/aa21543-13.html#page={{{2|1}}} {{{3|Planck-2013-XVII}}}]<br />
|planck2013-p13=[http://www.aanda.org/articles/aa/abs/2014/11/aa21540-13/aa21540-13.html#page={{{2|1}}} {{{3|Planck-2013-XVIII}}}]<br />
|planck2013-p14=[http://www.aanda.org/articles/aa/abs/2014/11/aa21526-13/aa21526-13.html#page={{{2|1}}} {{{3|Planck-2013-XIX}}}]<br />
|planck2013-p15=[http://www.aanda.org/articles/aa/abs/2014/11/aa21521-13/aa21521-13.html#page={{{2|1}}} {{{3|Planck-2013-XX}}}]<br />
|planck2013-p05b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21522-13/aa21522-13.html#page={{{2|1}}} {{{3|Planck-2013-XXI}}}]<br />
|planck2013-p17=[http://www.aanda.org/articles/aa/abs/2014/11/aa21569-13/aa21569-13.html#page={{{2|1}}} {{{3|Planck-2013-XXII}}}]<br />
|planck2013-p09=[http://www.aanda.org/articles/aa/abs/2014/11/aa21534-13/aa21534-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIII}}}]<br />
|planck2013-p09a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21554-13/aa21554-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIV}}}]<br />
|planck2013-p20=[http://www.aanda.org/articles/aa/abs/2014/11/aa21621-13/aa21621-13.html#page={{{2|1}}} {{{3|Planck-2013-XXV}}}]<br />
|planck2013-p19=[http://www.aanda.org/articles/aa/abs/2014/11/aa21546-13/aa21546-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVI}}}]<br />
|planck2013-pipaberration=[http://www.aanda.org/articles/aa/abs/2014/11/aa21556-13/aa21556-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVII}}}]<br />
|planck2013-p05=[http://www.aanda.org/articles/aa/abs/2014/11/aa21524-13/aa21524-13.html#page={{{2|1}}} {{{3|Planck-2013-XXVIII}}}]<br />
|planck2013-p05a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21523-13/aa21523-13.html#page={{{2|1}}} {{{3|Planck-2013-XXIX}}}]<br />
|planck2013-pip56=[http://www.aanda.org/articles/aa/abs/2014/11/aa22093-13/aa22093-13.html#page={{{2|1}}} {{{3|Planck-2013-XXX}}}]<br />
|planck2013-p01a=[http://www.aanda.org/articles/aa/abs/2014/11/aa23743-14/aa23743-14.html#page={{{2|1}}} {{{3|Planck-2013-XXXI}}}]<br />
|planck2013-p28=[http://www.sciops.esa.int/wikiSI/planckpla/index.php?title=Main_Page '''The Explanatory Supplement to the Planck 2013 results'''], Planck Collaboration, ESA, (2013).<br />
|planck2012-I=[http://www.aanda.org/articles/aa/pdf/2012/07/aa18731-11.pdf#page={{{2|1}}} {{{3|Planck-Int-I}}}]<br />
|planck2012-II=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19361-12.pdf#page={{{2|1}}} {{{3|Planck-Int-II}}}]<br />
|planck2012-III=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19398-12.pdf#page={{{2|1}}} {{{3|Planck-Int-III}}}]<br />
|planck2012-IV=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19519-12.pdf#page={{{2|1}}} {{{3|Planck-Int-IV}}}]<br />
|planck2012-V=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20040-12.pdf#page={{{2|1}}} {{{3|Planck-Int-V}}}]<br />
|planck2012-VI=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20039-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VI}}}]<br />
|planck2012-VII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20053-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VII}}}]<br />
|planck2012-VIII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20194-12.pdf#page={{{2|1}}} {{{3|Planck-Int-VIII}}}]<br />
|planck2012-IX=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20271-12.pdf#page={{{2|1}}} {{{3|Planck-Int-IX}}}]<br />
|planck2012-X=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20247-12.pdf#page={{{2|1}}} {{{3|Planck-Int-X}}}]<br />
|planck2012-XI=[http://www.aanda.org/articles/aa/pdf/2013/09/aa20941-12.pdf#page={{{2|1}}} {{{3|Planck-Int-XI}}}]<br />
|planck2013-XII=[http://www.aanda.org/articles/aa/pdf/2013/09/aa21160-13.pdf#page={{{2|1}}} {{{3|Planck-Int-XII}}}]<br />
|planck2013-XIII=[http://www.aanda.org/articles/aa/pdf/2014/01/aa21299-13.pdf#page={{{2|1}}} {{{3|Planck-Int-XIII}}}]<br />
|planck2013-XIV=[http://arxiv.org/pdf/1307.6815.pdf#page={{{2|1}}} {{{3|Planck-Int-XIV}}}] <br />
|planck2013-XV=[http://arxiv.org/pdf/1309.1357.pdf#page={{{2|1}}} {{{3|Planck-Int-XV}}}]<br />
|planck2013-XVI=[http://arxiv.org/pdf/1311.1657.pdf#page={{{2|1}}} {{{3|Planck-Int-XVI}}}]<br />
|planck2013-XVII=[http://arxiv.org/pdf/1312.5446.pdf#page={{{2|1}}} {{{3|Planck-Int-XVII}}}]<br />
|planck2011-1-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16464-11.pdf#page={{{2|1}}} {{{3|Planck-Early-I}}}]<br />
|planck2011-1-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16486-11.pdf#page={{{2|1}}} {{{3|Planck-Early-II}}}]<br />
|planck2011-1-4=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16480-11.pdf#page={{{2|1}}} {{{3|Planck-Early-III}}}]<br />
|planck2011-1-5=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16487-11.pdf#page={{{2|1}}} {{{3|Planck-Early-IV}}}]<br />
|planck2011-1-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16484-11.pdf#page={{{2|1}}} {{{3|Planck-Early-V}}}]<br />
|planck2011-1-7=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16462-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VI}}}]<br />
|planck2011-1-10=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16474-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VII}}}]<br />
|planck2011-1-10sup=[http://arxiv.org {{{3|Planck-Exp-Sup}}}]<br />
|planck2011-5-1a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16459-11.pdf#page={{{2|1}}} {{{3|Planck-Early-VIII}}}]<br />
|planck2011-5-1b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16460-11.pdf#page={{{2|1}}} {{{3|Planck-Early-IX}}}]<br />
|planck2011-5-2a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16457-11.pdf#page={{{2|1}}} {{{3|Planck-Early-X}}}]<br />
|planck2011-5-2b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16458-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XI}}}]<br />
|planck2011-5-2c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16489-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XII}}}]<br />
|planck2011-6-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16471-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIII}}}]<br />
|planck2011-6-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16475-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIV}}}]<br />
|planck2011-6-3a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16466-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XV}}}]<br />
|planck2011-6-4a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16454-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVI}}}]<br />
|planck2011-6-4b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16473-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVII}}}]<br />
|planck2011-6-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16461-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XVIII}}}]<br />
|planck2011-7-0=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16479-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XIX}}}]<br />
|planck2011-7-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16470-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XX}}}]<br />
|planck2011-7-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16455-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXI}}}]<br />
|planck2011-7-7a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16481-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXII}}}]<br />
|planck2011-7-7b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16472-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXIII}}}]<br />
|planck2011-7-12=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16485-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXIV}}}]<br />
|planck2011-7-13=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16483-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXV}}}]<br />
|planck2011-5-1c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa17430-11.pdf#page={{{2|1}}} {{{3|Planck-Early-XXVI}}}]<br />
|planck2011-6-3b=[http://www.aanda.org/articles/aa/pdf/2012/05/aa17825-11.pdf#page={{{2|1}}} {{{3|Planck-Swift-Fermi}}}]<br />
|ade2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13039-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-I}}}]<br />
|bersanelli2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12853-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-II}}}]<br />
|lamarre2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12975-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-III}}}]<br />
|leahy2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12855-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-IV}}}]<br />
|maffei2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12999-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-V}}}]<br />
|mandolesi2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12837-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VI}}}]<br />
|mennella2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12849-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VII}}}]<br />
|pajot2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13203-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-VIII}}}]<br />
|rosset2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13054-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-IX}}}]<br />
|sandri2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12891-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-X}}}]<br />
|tauber2010a=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12983-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-XI}}}]<br />
|tauber2010b=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12911-09.pdf#page={{{2|1}}} {{{3|Planck-PreLaunch-XII}}}]<br />
|villa2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12860-09.pdf#page={{{2|1}}}} {{{3|Planck-PreLaunch-XIII}}}]<br />
}}{{#tag:ref|{{#switch: {{{1}}}<br />
|planck2014-a01=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27101-15/aa27101-15.html'''Planck 2015 results. I. Overview of products and results'''], Planck Collaboration, 2016, A&amp;A, 594, A1.<br />
|planck2014-a03=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25818-15/aa25818-15.html'''Planck 2015 results. II. LFI processing'''], Planck Collaboration, 2016, A&amp;A, 594, A2.<br />
|planck2014-a04=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26998-15/aa26998-15.html'''Planck 2015 results. III. LFI systematics'''], Planck Collaboration, 2016, A&amp;A, 594, A3.<br />
|planck2014-a05=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25809-15/aa25809-15.html'''Planck 2015 results. IV. LFI beams and window functions'''], Planck Collaboration, 2016, A&amp;A, 594, A4.<br />
|planck2014-a06=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26632-15/aa26632-15.html'''Planck 2015 results. V. LFI calibration'''], Planck Collaboration, 2016, A&amp;A, 594, A5.<br />
|planck2014-a07=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25813-15/aa25813-15.html'''Planck 2015 results. VI. LFI mapmaking'''], Planck Collaboration, 2016, A&amp;A, 594, A6.<br />
|planck2014-a08=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25844-15/aa25844-15.html'''Planck 2015 results. VII. High Frequency Instrument data processing: Time-ordered information and beam processing'''], Planck Collaboration, 2016, A&amp;A, 594, A7.<br />
|planck2014-a09=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25820-15/aa25820-15.html'''Planck 2015 results. VIII. High Frequency Instrument data processing: Calibration and maps'''], Planck Collaboration, 2016, A&amp;A, 594, A8.<br />
|planck2014-a11=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25936-15/aa25936-15.html'''Planck 2015 results. XI. Diffuse component separation: CMB maps'''], Planck Collaboration, 2016, A&amp;A, 594, A9.<br />
|planck2014-a12=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25967-15/aa25967-15.html'''Planck 2015 results. X. Diffuse component separation: Foreground maps'''], Planck Collaboration, 2016, A&amp;A, 594, A10.<br />
|planck2014-a13=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26926-15/aa26926-15.html'''Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of cosmological parameters'''], Planck Collaboration, 2016, A&amp;A, 594, A11.<br />
|planck2014-a14=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27103-15/aa27103-15.html'''Planck 2015 results. XII. Full Focal Plane Simulations'''], Planck Collaboration, 2016, A&amp;A, 594, A12.<br />
|planck2014-a15=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25830-15/aa25830-15.html'''Planck 2015 results. XIII. Cosmological parameters'''], Planck Collaboration, 2016, A&amp;A, 594, A13.<br />
|planck2014-a16=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25814-15/aa25814-15.html'''Planck 2015 results. XIV. Dark energy and modified gravity'''], Planck Collaboration, 2016, A&amp;A, 594, A14.<br />
|planck2014-a17=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25941-15/aa25941-15.html'''Planck 2015 results. XV. Gravitational Lensing'''], Planck Collaboration, 2016, A&amp;A, 594, A15.<br />
|planck2014-a18=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26681-15/aa26681-15.html'''Planck 2015 results. XVI. Isotropy and statistics'''], Planck Collaboration, 2016, A&amp;A, 594, A16.<br />
|planck2014-a19=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25836-15/aa25836-15.html'''Planck 2015 results. XVII. Primordial non-Gaussianity'''], Planck Collaboration, 2016, A&amp;A, 594, A17.<br />
|planck2014-a20=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25829-15/aa25829-15.html'''Planck 2015 results. XVIII. Background geometry and topology of the Universe'''], Planck Collaboration, 2016, A&amp;A, 594, A18.<br />
|planck2014-a22=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25821-15/aa25821-15.html'''Planck 2015 results. XIX. Constraints on primordial magnetic fields'''], Planck Collaboration, 2016, A&amp;A, 594, A19.<br />
|planck2014-a24=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25898-15/aa25898-15.html'''Planck 2015 results. XX. Constraints on inflation'''], Planck Collaboration, 2016, A&amp;A, 594, A20.<br />
|planck2014-a26=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25831-15/aa25831-15.html'''Planck 2015 results. XXI. The integrated Sachs-Wolfe effect'''], Planck Collaboration, 2016, A&amp;A, 594, A21.<br />
|planck2014-a28=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25826-15/aa25826-15.html'''Planck 2015 results. XXII. A map of the thermal Sunyaev-Zeldovich effect'''], Planck Collaboration, 2016, A&amp;A, 594, A22.<br />
|planck2014-a29=[http://www.aanda.org/articles/aa/full_html/2016/10/aa27418-15/aa27418-15.html'''Planck 2015 results. XXIII. Thermal Sunyaev-Zeldovich effect–cosmic infrared background correlation'''], Planck Collaboration, 2016, A&amp;A, 594, A23.<br />
|planck2014-a30=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25833-15/aa25833-15.html'''Planck 2015 results. XXIV. Cosmology from Sunyaev-Zeldovich cluster counts '''], Planck Collaboration, 2016, A&amp;A, 594, A24.<br />
|planck2014-a31=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26803-15/aa26803-15.html'''Planck 2015 results. XXV. Diffuse low frequency Galactic foregrounds'''], Planck Collaboration, 2016, A&amp;A, 594, A25.<br />
|planck2014-a33=[http://arxiv.org '''A33 Zodiacal light'''], Planck Collaboration C33, in preparation, (2015).<br />
|planck2014-a35=[http://www.aanda.org/articles/aa/full_html/2016/10/aa26914-15/aa26914-15.html'''Planck 2015 results. XXVI. The second Planck catalogue of compact sources'''], Planck Collaboration, 2016, A&amp;A, 594, A26.<br />
|planck2014-a36=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25823-15/aa25823-15.html'''Planck 2015 results. XXVII. The second Planck catalogue of Sunyaev-Zeldovich sources'''], Planck Collaboration, 2016, A&amp;A, 594, A27.<br />
|planck2014-a37=[http://www.aanda.org/articles/aa/full_html/2016/10/aa25819-15/aa25819-15.html'''Planck 2015 results. XXVIII. The Planck catalogue of Galactic cold clumps'''], Planck Collaboration, 2016, A&amp;A, 594, A28.<br />
|pb2015=[http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.101301'''A joint analysis of BICEP2/Keck Array and Planck data'''], BICEP2/Keck Array, Planck Collaborations, PRL, 114, 101301 (2015).<br />
|planck2014-XVIII=[http://www.aanda.org/articles/aa/pdf/2015/01/aa23836-14.pdf'''Planck intermediate results. XVIII. The millimetre and submillimetre emission from planetary nebulae'''], Planck Collaboration Int. XVIII, A&A, '''573''', A6, (2015).<br />
|planck2014-XIX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24082-14.pdf'''Planck intermediate results. XIX. An overview of the polarized thermal emission from Galactic dust'''], Planck Collaboration Int. XIX, A&A, '''576''', A104, (2015).<br />
|planck2014-XX=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24086-14.pdf'''Planck intermediate results. XX. Comparison of polarized thermal emission from Galactic dust with simulations of MHD turbulence'''], Planck Collaboration Int. XX, A&A, '''576''', A105, (2015).<br />
|planck2014-XXI=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24087-14.pdf'''Planck intermediate results. XXI. Comparison of polarized thermal emission from Galactic dust at 353 GHz with optical interstellar polarization'''], Planck Collaboration Int. XXI, A&A, '''576''', A106, (2015).<br />
|planck2014-XXII=[http://www.aanda.org/articles/aa/pdf/2015/04/aa24088-14.pdf '''Planck intermediate results. XXII. Frequency dependence of thermal emission from Galactic dust in intensity and polarization'''], Planck Collaboration Int. XXII, A&A, '''576''', A107, (2015).<br />
|planck2014-XXIII=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24434-14.pdf '''Planck intermediate results. XXIII. Galactic plane emission components derived from Planck with ancillary data'''], Planck Collaboration Int. XXIII, A&A, '''580''', A13, (2015).<br />
|planck2014-XXIV=[http://www.aanda.org/articles/aa/pdf/2015/08/aa24496-14.pdf'''Planck intermediate results. XXIV. Constraints on variation of fundamental constants'''], Planck Collaboration Int. XXIV, A&A, '''580''', A22, (2015).<br />
|planck2014-XXV=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24643-14.pdf'''Planck intermediate results. XXV. The Andromeda Galaxy as seen by ''Planck'''''], Planck Collaboration Int. XXV, A&A, '''582''', A28, (2015).<br />
|planck2014-XXVI=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24674-14.pdf '''Planck intermediate results. XXVI. Optical identification and redshifts of ''Planck'' clusters with the RTT150 telescope'''], Planck Collaboration Int. XXVI, A&A, '''582''', A29, (2015).<br />
|planck2014-XXVII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24790-14.pdf'''Planck intermediate results. XXVII. High-redshift infrared galaxy overdensity candidates and lensed sources discovered by Planck and confirmed by Herschel-SPIRE'''], Planck Collaboration Int. XXVII, A&A, '''582''', A29, (2015).<br />
|planck2014-XXVIII=[http://www.aanda.org/articles/aa/pdf/2015/10/aa24955-14.pdf'''Planck intermediate results. XXVIII. Interstellar gas and dustin the Chamaeleon clouds as seen by ''Fermi'' LAT and ''Planck'''''], Planck Collaboration Int. XXVIII, A&A, '''582''', A30, (2015).<br />
|planck2014-XXIX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf''Planck intermediate results. XXIX. All-sky dust modelling with ''Planck'', IRAS, and WISE observations'''], Planck Collaboration Int. XXIX, A&A, '''586''', A132, (2016).<br />
|planck2014-XXX=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25034-14.pdf'''Planck intermediate results. XXX. The angular power spectrum of polarized dust emission at intermediate and high Galactic latitudes'''], Planck Collaboration Int. XXX, A&A, '''586''', A133, (2016).<br />
|planck2014-XXXI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25022-14.pdf'''Planck intermediate results. XXXI. Microwave survey of Galactic supernova remnants'''], Planck Collaboration Int. XXXI, A&A, '''586''', A134, (2016).<br />
|planck2014-XXXII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25044-14.pdf'''Planck intermediate results. XXXII. The relative orientation between the magnetic field and structures traced by interstellar dust'''], Planck Collaboration Int. XXXII, A&A, '''586''', A135, (2016).<br />
|planck2014-XXXIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25305-14.pdf'''Planck intermediate results. XXXIII. Signature of the magnetic field geometry of interstellar filaments in dust polarization maps'''], Planck Collaboration Int. XXXIII, A&A, '''586''', A136, (2016).<br />
|planck2015-XXXIV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25616-15.pdf'''Planck intermediate results. XXXIV. The magnetic field structure in the Rosette Nebula'''], Planck Collaboration Int. XXX A&A, 586, A137, (2016).<br />
|planck2015-XXXV=[http://www.aanda.org/articles/aa/pdf/2016/02/aa25896-15.pdf'''Planck intermediate results. XXXV. Probing the role of the magnetic field in the formation of structure in molecular clouds'''], Planck Collaboration Int. XXX A&A, 586, A138, (2016).<br />
|planck2015-XXXVI=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26345-15.pdf'''Planck intermediate results. XXXVI. Optical identification and redshifts of Planck SZ sources with telescopes in the Canary Islands Observatories'''], Planck Collaboration Int. XXX A&A, 586, A139, (2016).<br />
|planck2015-XXXVII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26328-15.pdf'''Planck intermediate results. XXXVII. Evidence of unbound gas from the kinetic Sunyaev-Zeldovich effect.'''], Planck Collaboration Int. XXX A&A, 586, A140, (2016).<br />
|planck2015-XXXVIII=[http://www.aanda.org/articles/aa/pdf/2016/02/aa26506-15.pdf'''Planck intermediate results. XXXVIII. E- and B-modes of dust polarization from the magnetized filamentary structure of the interstellar medium'''], Planck Collaboration Int. XXX A&A, 586, A141, (2016).<br />
<br />
|planck2015-XXXIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27206-15/aa27206-15.html'''Planck intermediate results. XXXIX. The Planck list of high-redshift source candidates'''], Planck Collaboration Int. XXXIX A&A, 596, A100, (2016).<br />
|planck2015-XL=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27743-15/aa27743-15.html'''Planck intermediate results. XL. The Sunyaev-Zeldovich signal from the Virgo cluster'''], Planck Collaboration Int. XL A&A, 596, A101, (2016).<br />
|planck2015-XLI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27932-15/aa27932-15.html'''Planck intermediate results. XLI. A map of lensing-induced B-modes'''], Planck Collaboration Int. XLI A&A, 596, A102, (2016).<br />
|planck2016-XLII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28033-15/aa28033-15.html'''Planck intermediate results. XLII. Large-scale Galactic magnetic fields'''], Planck Collaboration Int. XLII A&A, 596, A103, (2016).<br />
|planck2016-XLIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28522-16/aa28522-16.html'''Planck intermediate results. XLIII. The spectral energy distribution of dust in clusters of galaxies'''], Planck Collaboration Int. XLIII A&A, 596, A104, (2016).<br />
|planck2016-XLIV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28636-16/aa28636-16.html'''Planck intermediate results. XLIV. The structure of the Galactic magnetic field from dust polarization maps of the southern Galactic cap'''], Planck Collaboration Int. XLIV A&A, 596, A105, (2016).<br />
|planck2016-XLV=[http://www.aanda.org/articles/aa/full_html/2016/12/aa27780-15/aa27780-15.html'''Planck intermediate results. XLV. Radio spectra of northern extragalactic radio sources'''], Planck Collaboration Int. XLV A&A, 596, A106, (2016).<br />
|planck2016-XLVI=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28890-16/aa28890-16.html'''Planck intermediate results. XLVI. Reduction of large-scale systematic effects in HFI polarization maps and estimation of the reionization optical depth'''], Planck Collaboration Int. XLVI A&A, 596, A107, (2016).<br />
|planck2016-XLVII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa28897-16/aa28897-16.html'''Planck intermediate results. XLVII. Constraints on reionization history'''], Planck Collaboration Int. XLVII A&A, 596, A108, (2016).<br />
|planck2016-XLVIII=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29022-16/aa29022-16.html'''Planck intermediate results. XLVIII. Disentangling Galactic dust emission and cosmic infrared background anisotropies'''], Planck Collaboration Int. XLVIII A&A, 596, A109, (2016).<br />
|planck2016-XLIX=[http://www.aanda.org/articles/aa/full_html/2016/12/aa29018-16/aa29018-16.html'''Planck intermediate results. XLIX. Parity-violation constraints from polarization data'''], Planck Collaboration Int. XLIX A&A, 596, A110, (2016).<br />
|planck2016-L=[http://xxx.lanl.gov/abs/1606.07335'''Planck intermediate results. L. Evidence for spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B-mode analysis'''], Planck Collaboration Int. L A&A, 599, A55, (2017)<br />
|planck2016-LI=[http://xxx.lanl.gov/abs/1608.02487'''Planck intermediate results. LI. Features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters'''], Planck Collaboration Int. LI A&A, 607, A95, (2017)<br />
|planck2017-LII=[https://www.aanda.org/articles/aa/abs/2017/11/aa30311-16/aa30311-16.html'''Planck intermediate results. LII. Planets flux densities'''], Planck Collaboration Int. LII A&A, 607, A122, (2017)<br />
|planck2013-p01=[http://www.aanda.org/articles/aa/abs/2014/11/aa21529-13/aa21529-13.html#page={{{2|1}}}'''Planck 2013 results. I. Overview of Products and Results'''], Planck Collaboration, 2014, A&A, 571, A1<br />
|planck2013-p02=[http://www.aanda.org/articles/aa/abs/2014/11/aa21550-13/aa21550-13.html#page={{{2|1}}}'''Planck 2013 results. II. Low Frequency Instrument data processing'''], Planck Collaboration, 2014, A&A, 571, A2<br />
|planck2013-p02a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21574-13/aa21574-13.html#page={{{2|1}}}'''Planck 2013 results. III. Low Frequency Instrument systematic uncertainties'''], Planck Collaboration, 2014, A&A, 571, A3<br />
|planck2013-p02d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21544-13/aa21544-13.html#page={{{2|1}}}'''Planck 2013 results. IV. Low Frequency Instrument beams and window functions'''], Planck Collaboration, 2014, A&A, 571, A4<br />
|planck2013-p02b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21527-13/aa21527-13.html#page={{{2|1}}}'''Planck 2013 results. V. LFI Calibration'''], Planck Collaboration, 2014, A&A, 571, A5<br />
|planck2013-p03=[http://www.aanda.org/articles/aa/abs/2014/11/aa21570-13/aa21570-13.html#page={{{2|1}}} '''Planck 2013 results. VI. High Frequency Instrument Data Processing'''], Planck Collaboration, 2014, A&A, 571, A6<br />
|planck2013-p03c=[http://www.aanda.org/articles/aa/abs/2014/11/aa21535-13/aa21535-13.html#page={{{2|1}}}'''Planck 2013 results. VII. HFI time response and beams'''], Planck Collaboration, 2014, A&A, 571, A7<br />
|planck2013-p03b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21538-13/aa21538-13.html#page={{{2|1}}}'''Planck 2013 results. VIII. HFI photometric calibration and Map-making'''], Planck Collaboration, 2014, A&A, 571, A8<br />
|planck2013-p03d=[http://www.aanda.org/articles/aa/abs/2014/11/aa21531-13/aa21531-13.html#page={{{2|1}}}'''Planck 2013 results. IX. HFI spectral response'''], Planck Collaboration, 2014, A&A, 571, A9<br />
|planck2013-p03e=[http://www.aanda.org/articles/aa/abs/2014/11/aa21577-13/aa21577-13.html#page={{{2|1}}}'''Planck 2013 results. X. HFI energetic particle effects: characterization, removal, and simulation'''], Planck Collaboration, 2014, A&A, 571, A10<br />
|planck2013-p06=[http://www.aanda.org/articles/aa/abs/2014/11/aa23195-13/aa23195-13.html#page={{{2|1}}}'''Planck 2013 results. XI. Component separation'''], Planck Collaboration, 2014, A&A, 571, A11<br />
|planck2013-p06b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21580-13/aa21580-13.html#page={{{2|1}}}'''Planck 2013 results. XII. All-sky model of thermal dust emission'''], Planck Collaboration, 2014, A&A, 571, A12<br />
|planck2013-p03a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21553-13/aa21553-13.html#page={{{2|1}}}'''Planck 2013 results. XIII. Galactic CO emission''], Planck Collaboration, 2014, A&A, 571, A13<br />
|planck2013-pip88=[http://www.aanda.org/articles/aa/abs/2014/11/aa21562-13/aa21562-13.html#page={{{2|1}}}'''Planck 2013 results. XIV. Zodiacal emission'''], Planck Collaboration, 2014, A&A, 571, A14<br />
|planck2013-p08=[http://www.aanda.org/articles/aa/abs/2014/11/aa21573-13/aa21573-13.html#page={{{2|1}}}'''Planck 2013 results. XV. CMB power spectra and likelihood'''], Planck Collaboration, 2014, A&A, 571, A15<br />
|planck2013-p11=[http://www.aanda.org/articles/aa/abs/2014/11/aa21591-13/aa21591-13.html#page={{{2|1}}}'''Planck 2013 results. XVI. Cosmological parameters'''], Planck Collaboration, 2014, A&A, 571, A16<br />
|planck2013-p12=[http://www.aanda.org/articles/aa/abs/2014/11/aa21543-13/aa21543-13.html#page={{{2|1}}}'''Planck 2013 results. XVII. Gravitational lensing by large-scale structure'''], Planck Collaboration, 2014, A&A, 571, A17<br />
|planck2013-p13=[http://www.aanda.org/articles/aa/abs/2014/11/aa21540-13/aa21540-13.html#page={{{2|1}}}'''Planck 2013 results. XVIII. The gravitational lensing-infrared background correlation'''], Planck Collaboration, 2014, A&A, 571, A18<br />
|planck2013-p14=[http://www.aanda.org/articles/aa/abs/2014/11/aa21526-13/aa21526-13.html#page={{{2|1}}}'''Planck 2013 results. XIX. The integrated Sachs-Wolfe effect'''], Planck Collaboration, 2014, A&A, 571, A19<br />
<br />
|planck2013-p15=[http://www.aanda.org/articles/aa/abs/2014/11/aa21521-13/aa21521-13.html#page={{{2|1}}}'''Planck 2013 results. XX. Cosmology from Planck Sunyaev Zeldovich cluster counts'''], Planck Collaboration, 2014, A&A, 571, A20<br />
|planck2013-p05b=[http://www.aanda.org/articles/aa/abs/2014/11/aa21522-13/aa21522-13.html#page={{{2|1}}}'''Planck 2013 results. XXI. Power spectrum and high-order statistics of the Planck all-sky Compton parameter map'''], Planck Collaboration, 2014, A&A, 571, A21<br />
|planck2013-p17=[http://www.aanda.org/articles/aa/abs/2014/11/aa21569-13/aa21569-13.html#page={{{2|1}}}'''Planck 2013 results. XXII. Constraints on inflation'''], Planck Collaboration, 2014, A&A, 571, A22<br />
|planck2013-p09=[http://www.aanda.org/articles/aa/abs/2014/11/aa21534-13/aa21534-13.html#page={{{2|1}}}'''Planck 2013 results. XXIII. Isotropy and statistics of the CMB'''], Planck Collaboration, 2014, A&A, 571, A23<br />
|planck2013-p09a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21554-13/aa21554-13.html#page={{{2|1}}}'''Planck 2013 results. XXIV. Constraints on primordial non-Gaussianity'''], Planck Collaboration, 2014, A&A, 571, A24<br />
|planck2013-p20=[http://www.aanda.org/articles/aa/abs/2014/11/aa21621-13/aa21621-13.html#page={{{2|1}}}'''Planck 2013 results. XXV. Searches for cosmic strings and other topological defects'''], Planck Collaboration, 2014, A&A, 571, A25<br />
|planck2013-p19=[http://www.aanda.org/articles/aa/abs/2014/11/aa21546-13/aa21546-13.html#page={{{2|1}}}'''Planck 2013 results. XXVI. Geometry and topology of the Universe'''], Planck Collaboration, 2014, A&A, 571, A26<br />
|planck2013-pipaberration=[http://www.aanda.org/articles/aa/abs/2014/11/aa21556-13/aa21556-13.html#page={{{2|1}}}'''Planck 2013 results. XXVII. Doppler boosting of the CMB: Eppur si muove'''], Planck Collaboration, 2014, A&A, 571, A27<br />
|planck2013-p05=[http://www.aanda.org/articles/aa/abs/2014/11/aa21524-13/aa21524-13.html#page={{{2|1}}}'''Planck 2013 results. XXVIII. The Planck Catalogue of Compact Sources'''], Planck Collaboration, 2014, A&A, 571, A28<br />
|planck2013-p05a=[http://www.aanda.org/articles/aa/abs/2014/11/aa21523-13/aa21523-13.html#page={{{2|1}}}'''Planck 2013 results. XXIX. The Planck Catalogue of Sunyaev-Zeldovich sources'''], Planck Collaboration, 2014, A&A, 571, A29<br />
|planck2013-pip56=[http://www.aanda.org/articles/aa/abs/2014/11/aa22093-13/aa22093-13.html#page={{{2|1}}}'''Planck 2013 results. XXX. Cosmic infrared background measurements and implications for star formation'''], Planck Collaboration, 2014, A&A, 571, A30<br />
|planck2013-p01a=[http://www.aanda.org/articles/aa/pdf/2014/11/aa23743-14.pdf#page={{{2|1}}}'''Planck 2013 results. XXXI. Consistency of Planck data'''], Planck Collaboration, 2014, A&A, 571, A31<br />
|planck2013-p28=[http://www.sciops.esa.int/wikiSI/planckpla/index.php?title=Main_Page '''The Explanatory Supplement to the Planck 2013 results'''], Planck Collaboration, ESA, (2013).<br />
|planck2012-I=[http://www.aanda.org/articles/aa/pdf/2012/07/aa18731-11.pdf#page={{{2|1}}} '''Planck intermediate results. I. Further validation of new Planck clusters with XMM-Newton'''], Planck Collaboration Int. I, A&A, '''543''', A102, (2012).<br />
|planck2012-II=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19361-12.pdf#page={{{2|1}}}'''Planck intermediate results. II. Comparison of Sunyaev-Zeldovich measurements from Planck and from the Arcminute Microkelvin Imager for 11 galaxy clusters'''], Planck, AMI Collaborations, A&A, '''550''', A128, (2013).<br />
|planck2012-III=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19398-12.pdf#page={{{2|1}}}'''Planck intermediate results. III. The relation between galaxy cluster mass and Sunyaev-Zeldovich signal'''], Planck Collaboration Int. III, A&A, '''550''', A129, (2013).<br />
|planck2012-IV=[http://www.aanda.org/articles/aa/pdf/2013/02/aa19519-12.pdf#page={{{2|1}}}'''Planck intermediate results. IV. The XMM-Newton validation programme for new Planck clusters'''], Planck Collaboration Int. IV, A&A, '''550''', A130, (2013).<br />
|planck2012-V=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20040-12.pdf#page={{{2|1}}}'''Planck intermediate results. V. Pressure profiles of galaxy clusters from the Sunyaev-Zeldovich effect'''], Planck Collaboration Int. V, A&A, '''550''', A131, (2013).<br />
|planck2012-VI=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20039-12.pdf#page={{{2|1}}}'''Planck intermediate results. VI. The dynamical structure of PLCKG214.6+37.0, a Planck discovered triple system of galaxy clusters'''], Planck Collaboration Int. VI, A&A, '''550''', A132, (2013).<br />
|planck2012-VII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20053-12.pdf#page={{{2|1}}}'''Planck intermediate results. VII. Statistical properties of infrared and radio extragalactic sources from the Planck Early Release Compact Source Catalogue at frequencies between 100 and 857 GHz'''], Planck Collaboration Int. VII, A&A, '''550''', A133, (2013).<br />
|planck2012-VIII=[http://www.aanda.org/articles/aa/pdf/2013/02/aa20194-12.pdf#page={{{2|1}}}'''Planck intermediate results. VIII. Filaments between interacting clusters'''], Planck Collaboration Int. VIII, A&A, '''550''', A134, (2013).<br />
|planck2012-IX=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20271-12.pdf#page={{{2|1}}}'''Planck intermediate results. IX. Detection of the Galactic haze with Planck'''], Planck Collaboration Int. IX, A&A, '''554''', A139, (2013).<br />
|planck2012-X=[http://www.aanda.org/articles/aa/pdf/2013/06/aa20247-12.pdf#page={{{2|1}}}'''Planck intermediate results. X. Physics of the hot gas in the Coma cluster'''], Planck Collaboration Int. X, A&A, '''554''', A140, (2013).<br />
|planck2012-XI=[http://www.aanda.org/articles/aa/pdf/2013/09/aa20941-12.pdf#page={{{2|1}}}'''Planck intermediate results. XI. The gas content of dark matter halos: the Sunyaev-Zeldovich-stellar mass relation for locally brightest galaxies'''], Planck Collaboration Int. XI, A&A, '''557''', A52, (2013).<br />
|planck2013-XII=[http://www.aanda.org/articles/aa/pdf/2013/09/aa21160-13.pdf#page={{{2|1}}}'''Planck intermediate results. XII. Diffuse Galactic components in the Gould Belt System'''], Planck Collaboration Int. XII, A&A, '''557''', A53, (2013).<br />
|planck2013-XIII=[http://www.aanda.org/articles/aa/pdf/2014/01/aa21299-13.pdf#page={{{2|1}}}'''Planck intermediate results. XIII. Constraints on peculiar velocities'''], Planck Collaboration Int. XIII, A&A, '''561''', A97, (2013).<br />
|planck2013-XIV=[http://arxiv.org/pdf/1307.6815.pdf#page={{{2|1}}}'''Planck intermediate results. XIV. Dust emission at millimetre wavelengths in the Galactic plane'''], Planck Collaboration Int. XIV, A&A, in press, (2014).<br />
|planck2013-XV=[http://arxiv.org/pdf/1309.1357.pdf#page={{{2|1}}}'''Planck intermediate results. XV. A study of anomalous microwave emission in Galactic clouds'''], Planck Collaboration Int. XV, A&A, in press, (2014).<br />
|planck2013-XVI=[http://arxiv.org/pdf/1311.1657.pdf#page={{{2|1}}}'''Planck intermediate results. XVI. Profile likelihoods for cosmological parameters'''], Planck Collaboration Int. XVI, A&A, in press, (2014).<br />
|planck2013-XVII=[http://arxiv.org/pdf/1312.5446.pdf#page={{{2|1}}}'''Planck intermediate results. XVII. Emission of dust in the diffuse interstellar medium from the far-infrared to microwave frequencies'''], Planck Collaboration Int. XVII, A&A, in press, (2014).<br />
|planck2011-1-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16464-11.pdf#page={{{2|1}}}'''Planck early results. I. The Planck mission'''], Planck Collaboration I, A&A, '''536''', A1, (2011).<br />
|planck2011-1-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16486-11.pdf#page={{{2|1}}}'''Planck early results. II. The thermal performance of Planck'''], Planck Collaboration II, A&A, '''536''', A2, (2011).<br />
|planck2011-1-4=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16480-11.pdf#page={{{2|1}}}'''Planck early results. III. First assessment of the Low Frequency Instrument in-flight performance'''], A. Mennella, R. C. Butler, A. Curto, F. Cuttaia, R. J. Davis, J. Dick, M. Frailis, S. Galeotta, A. Gregorio, H. Kurki-Suonio, C. R. Lawrence, S. Leach, J. P. Leahy, S. Lowe, D. Maino, N. Mandolesi, M. Maris, E. Mart&iacute;nez-Gonz&aacute;lez, P. R. Meinhold, G. Morgante, D. Pearson, F. Perrotta, G. Polenta, T. Poutanen, M. Sandri, M. D. Seiffert, A.-S. Suur-Uski, D. Tavagnacco, L. Terenzi, M. Tomasi, J. Valiviita, F. Villa, R. Watson, A. Wilkinson, A. Zacchei, A. Zonca, B. Aja, E. Artal, C. Baccigalupi, A. J. Banday, R. B. Barreiro, J. G. Bartlett, N. Bartolo, P. Battaglia, K. Bennett, A. Bonaldi, L. Bonavera, J. Borrill, F. R. Bouchet, C. Burigana, P. Cabella, B. Cappellini, X. Chen, L. Colombo, M. Cruz, L. Danese, O. D'Arcangelo, R. D. Davies, G. de Gasperis, A. de Rosa, G. de Zotti, C. Dickinson, J. M. Diego, S. Donzelli, G. Efstathiou, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, S. Foley, C. Franceschet, E. Franceschi, T. C. Gaier, R. T. G&eacute;nova-Santos, D. George, F. G&oacute;mez, J. Gonz&aacute;lez-Nuevo, K. M. G&oacute;rski, A. Gruppuso, F. K. Hansen, D. Herranz, J. M. Herreros, R. J. Hoyland, N. Hughes, J. Jewell, P. Jukkala, M. Juvela, P. Kangaslahti, E. Keih&auml;nen, R. Keskitalo, V.-H. Kilpia, T. S. Kisner, J. Knoche, L. Knox, M. Laaninen, A. L&auml;hteenm&auml;ki, J.-M. Lamarre, R. Leonardi, J. Le&oacute;n-Tavares, P. Leutenegger, P. B. Lilje, M. L&oacute;pez-Caniego, P. M. Lubin, M. Malaspina, D. Marinucci, M. Massardi, S. Matarrese, F. Matthai, A. Melchiorri, L. Mendes, M. Miccolis, M. Migliaccio, S. Mitra, A. Moss, P. Natoli, R. Nesti, H. U. N&oslash;rgaard-Nielsen, L. Pagano, R. Paladini, D. Paoletti, B. Partridge, F. Pasian, V. Pettorino, D. Pietrobon, M. Pospieszalski, G. Pr&eacute;zeau, M. Prina, P. Procopio, J.-L. Puget, C. Quercellini, J. P. Rachen, R. Rebolo, M. Reinecke, S. Ricciardi, G. Robbers, G. Rocha, N. Roddis, J. A. Rubi&ntilde;o-Mart&iacute;n, M. Savelainen, D. Scott, R. Silvestri, A. Simonetto, P. Sjoman, G. F. Smoot, C. Sozzi, L. Stringhetti, J. A. Tauber, G. Tofani, L. Toffolatti, J. Tuovinen, M. T&uuml;rler, G. Umana, L. Valenziano, J. Varis, P. Vielva, N. Vittorio, L. A. Wade, C. Watson, S. D. M. White, F. Winder, A&A, '''536''', A3, (2011).<br />
|planck2011-1-5=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16487-11.pdf#page={{{2|1}}}'''Planck early results, IV. First assessment of the High Frequency Instrument in-flight performance'''], Planck HFI Core Team, A&A, '''536''', A4, (2011).<br />
|planck2011-1-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16484-11.pdf#page={{{2|1}}}'''Planck early results. V. The Low Frequency Instrument data processing'''], A. Zacchei, D. Maino, C. Baccigalupi, M. Bersanelli, A. Bonaldi, L. Bonavera, C. Burigana, R. C. Butler, F. Cuttaia, G. de Zotti, J. Dick, M. Frailis, S. Galeotta, J. Gonz&aacute;lez-Nuevo, K. M. G&oacute;rski, A. Gregorio, E. Keih&auml;nen, R. Keskitalo, J. Knoche, H. Kurki-Suonio, C. R. Lawrence, S. Leach, J. P. Leahy, M. L&oacute;pez-Caniego, N. Mandolesi, M. Maris, F. Matthai, P. R. Meinhold, A. Mennella, G. Morgante, N. Morisset, P. Natoli, F. Pasian, F. Perrotta, G. Polenta, T. Poutanen, M. Reinecke, S. Ricciardi, R. Rohlfs, M. Sandri, A.-S. Suur-Uski, J. A. Tauber, D. Tavagnacco, L. Terenzi, M. Tomasi, J. Valiviita, F. Villa, A. Zonca, A. J. Banday, R. B. Barreiro, J. G. Bartlett, N. Bartolo, L. Bedini, K. Bennett, P. Binko, J. Borrill, F. R. Bouchet, M. Bremer, P. Cabella, B. Cappellini, X. Chen, L. Colombo, M. Cruz, A. Curto, L. Danese, R. D. Davies, R. J. Davis, G. de Gasperis, A. de Rosa, G. de Troia, C. Dickinson, J. M. Diego, S. Donzelli, U. D&ouml;rl, G. Efstathiou, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, E. Franceschi, T. C. Gaier, F. Gasparo, R. T. G&eacute;nova-Santos, G. Giardino, F. G&oacute;mez, A. Gruppuso, F. K. Hansen, R. Hell, D. Herranz, W. Hovest, M. Huynh, J. Jewell, M. Juvela, T. S. Kisner, L. Knox, A. L&auml;hteenm&auml;ki, J.-M. Lamarre, R. Leonardi, J. Le&oacute;n-Tavares, P. B. Lilje, P. M. Lubin, G. Maggio, D. Marinucci, E. Mart&iacute;nez-Gonz&aacute;lez, M. Massardi, S. Matarrese, M. T. Meharga, A. Melchiorri, M. Migliaccio, S. Mitra, A. Moss, H. U. N&oslash;rgaard-Nielsen, L. Pagano, R. Paladini, D. Paoletti, B. Partridge, D. Pearson, V. Pettorino, D. Pietrobon, G. Pr&eacute;zeau, P. Procopio, J.-L. Puget, C. Quercellini, J. P. Rachen, R. Rebolo, G. Robbers, G. Rocha, J. A. Rubi&ntilde;o-Mart&iacute;n, E. Salerno, M. Savelainen, D. Scott, M. D. Seiffert, J. I. Silk, G. F. Smoot, J. Sternberg, F. Stivoli, R. Stompor, G. Tofani, L. Toffolatti, J. Tuovinen, M. T&uuml;rler, G. Umana, P. Vielva, N. Vittorio, C. Vuerli, L. A. Wade, R. Watson, S. D. M. White, A. Wilkinson, A&A, '''536''', A5, (2011).<br />
|planck2011-1-7=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16462-11.pdf#page={{{2|1}}}'''Planck early results. VI. The High Frequency Instrument data processing'''], Planck HFI Core Team, A&A, '''536''', A6, (2011).<br />
|planck2011-1-10=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16474-11.pdf#page={{{2|1}}}'''Planck early results. VII. The Early Release Compact Source Catalogue'''], Planck Collaboration VII, A&A, '''536''', A7, (2011).<br />
|planck2011-1-10sup=[http://arxiv.org '''The Explanatory Supplement to the Planck Early Release Compact Source Catalogue'''], Planck Collaboration, ESA, (2011).<br />
|planck2011-5-1a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16459-11.pdf#page={{{2|1}}}'''Planck early results. VIII. The all-sky early Sunyaev-Zeldovich cluster sample'''], Planck Collaboration VIII, A&A, '''536''', A8, (2011).<br />
|planck2011-5-1b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16460-11.pdf#page={{{2|1}}}'''Planck early results. IX. XMM-Newton follow-up for validation of Planck cluster candidates'''], Planck Collaboration IX, A&A, '''536''', A9, (2011).<br />
|planck2011-5-2a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16457-11.pdf#page={{{2|1}}}'''Planck early results. X. Statistical analysis of Sunyaev-Zeldovich scaling relations for X-ray galaxy clusters'''], Planck Collaboration X, A&A, '''536''', A10, (2011).<br />
|planck2011-5-2b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16458-11.pdf#page={{{2|1}}}'''Planck early results. XI. Calibration of the local galaxy cluster Sunyaev-Zeldovich scaling relations'''], Planck Collaboration XI, A&A, '''536''', A11, (2011).<br />
|planck2011-5-2c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16489-11.pdf#page={{{2|1}}}'''Planck early results. XII. Cluster Sunyaev-Zeldovich optical Scaling relations'''], Planck Collaboration XII, A&A, '''536''', A12, (2011).<br />
|planck2011-6-1=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16471-11.pdf#page={{{2|1}}}'''Planck early results. XIII. Statistical properties of extragalactic radio sources in the Planck Early Release Compact Source Catalogue'''], Planck Collaboration XIII, A&A, '''536''', A13, (2011).<br />
|planck2011-6-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16475-11.pdf#page={{{2|1}}}'''Planck early results. XIV. Early Release Compact Source Catalogue validation and extreme radio sources'''], Planck Collaboration XIV, A&A, '''536''', A14, (2011).<br />
|planck2011-6-3a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16466-11.pdf#page={{{2|1}}}'''Planck early results. XV. Spectral energy distributions and radio continuum spectra of northern extragalactic radio sources'''], Planck Collaboration XV, A&A, '''536''', A15, (2011).<br />
|planck2011-6-4a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16454-11.pdf#page={{{2|1}}}'''Planck early results. XVI. The Planck view of nearby galaxies'''], Planck Collaboration XVI, A&A, '''536''', A16, (2011).<br />
|planck2011-6-4b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16473-11.pdf#page={{{2|1}}}'''Planck early results. XVII. Origin of the submillimetre excess dust emission in the Magellanic Clouds'''], Planck Collaboration XVII, A&A, '''536''', A17, (2011).<br />
|planck2011-6-6=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16461-11.pdf#page={{{2|1}}}'''Planck early results. XVIII. The power spectrum of cosmic infrared background anisotropies'''], Planck Collaboration XVIII, A&A, '''536''', A18, (2011).<br />
|planck2011-7-0=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16479-11.pdf#page={{{2|1}}}'''Planck early results. XIX. All-sky temperature and dust optical <br />
depth from Planck and IRAS. Constraints on the dark gas in our Galaxy'''], Planck Collaboration XIX, A&A, '''536''', A19, (2011).<br />
|planck2011-7-2=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16470-11.pdf#page={{{2|1}}}'''Planck early results. XX. New light on anomalous microwave emission from spinning dust grains'''], Planck Collaboration XX, A&A, '''536''', A20, (2011).<br />
|planck2011-7-3=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16455-11.pdf#page={{{2|1}}}'''Planck early results. XXI. Properties of the interstellar medium in the Galactic plane'''], Planck Collaboration XXI, A&A, '''536''', A21, (2011).<br />
|planck2011-7-7a=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16481-11.pdf#page={{{2|1}}}'''Planck early results. XXII. The submillimetre properties of a sample of Galactic cold clumps'''], Planck Collaboration XXII, A&A, '''536''', A22, (2011).<br />
|planck2011-7-7b=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16472-11.pdf#page={{{2|1}}}'''Planck early results. XXIII. The Galactic cold core population revealed by the first all-sky survey'''], Planck Collaboration XXIII, A&A, '''536''', A23, (2011).<br />
|planck2011-7-12=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16485-11.pdf#page={{{2|1}}}'''Planck early results. XXIV. Dust in the diffuse interstellar medium and the Galactic halo'''], Planck Collaboration XXIV, A&A, '''536''', A24, (2011).<br />
|planck2011-7-13=[http://www.aanda.org/articles/aa/pdf/2011/12/aa16483-11.pdf#page={{{2|1}}}'''Planck early results. XXV. Thermal dust in nearby molecular clouds'''], Planck Collaboration XXV, A&A, '''536''', A25, (2011).<br />
|planck2011-5-1c=[http://www.aanda.org/articles/aa/pdf/2011/12/aa17430-11.pdf#page={{{2|1}}}'''Planck early results. XXVI. Detection with Planck and confirmation by XMM-Newton of PLCK G266.6-27.3, an exceptionally X-ray luminous and massive galaxy cluster at z 1'''], Planck Collaboration XXVI, A&A, '''536''', A26, (2011).<br />
|planck2011-6-3b=[http://www.aanda.org/articles/aa/pdf/2012/05/aa17825-11.pdf#page={{{2|1}}}'''Simultaneous Planck, Swift, and Fermi observations of X-ray and $\gamma$-ray selected blazars'''], P. Giommi, G. Polenta, A. L&auml;hteenm&auml;ki, D. J. Thompson, M. Capalbi, S. Cutini, D. Gasparrini, J. Gonz&aacute;lez-Nuevo, J. Le&oacute;n-Tavares, M. L&oacute;pez-Caniego, M. N. Mazziotta, C. Monte, M. Perri, S. Rain&ograve;, G. Tosti, A. Tramacere, F. Verrecchia, H. D. Aller, M. F. Aller, E. Angelakis, D. Bastieri, A. Berdyugin, A. Bonaldi, L. Bonavera, C. Burigana, D. N. Burrows, S. Buson, E. Cavazzuti, G. Chincarini, S. Colafrancesco, L. Costamante, F. Cuttaia, F. D'Ammando, G. de Zotti, M. Frailis, L. Fuhrmann, S. Galeotta, F. Gargano, N. Gehrels, N. Giglietto, F. Giordano, M. Giroletti, E. Keih&auml;nen, O. King, T. P. Krichbaum, A. Lasenby, N. Lavonen, C. R. Lawrence, C. Leto, E. Lindfors, N. Mandolesi, M. Massardi, W. Max-Moerbeck, P. F. Michelson, M. Mingaliev, P. Natoli, I. Nestoras, E. Nieppola, K. Nilsson, B. Partridge, V. Pavlidou, T. J. Pearson, P. Procopio, J. P. Rachen, A. Readhead, R. Reeves, A. Reimer, R. Reinthal, S. Ricciardi, J. Richards, D. Riquelme, J. Saarinen, A. Sajina, M. Sandri, P. Savolainen, A. Sievers, A. Sillanp&auml;&auml;, Y. Sotnikova, M. Stevenson, G. Tagliaferri, L. Takalo, J. Tammi, D. Tavagnacco, L. Terenzi, L. Toffolatti, M. Tornikoski, C. Trigilio, M. Turunen, G. Umana, H. Ungerechts, F. Villa, J. Wu, A. Zacchei, J. A. Zensus, X. Zhou, A&A, '''541''', A160, (2012).<br />
|ade2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13039-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The optical architecture of the HFI'''], P. A. R. Ade, G. Savini, R. Sudiwala, C. Tucker, A. Catalano, S. Church, R. Colgan, F. X. Desert, E. Gleeson, W. C. Jones, J.-M. Lamarre, A. Lange, Y. Longval, B. Maffei, J. A. Murphy, F. Noviello, F. Pajot, J.-L. Puget, I. Ristorcelli, A. Woodcraft, V. Yurchenko, A&A, '''520''', A11+, (2010).<br />
|bersanelli2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12853-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Design and description of the Low Frequency Instrument'''], M. Bersanelli, N. Mandolesi, R. C. Butler, A. Mennella, F. Villa, B. Aja, E. Artal, E. Artina, C. Baccigalupi, M. Balasini, G. Baldan, A. Banday, P. Bastia, P. Battaglia, T. Bernardino, E. Blackhurst, L. Boschini, C. Burigana, G. Cafagna, B. Cappellini, F. Cavaliere, F. Colombo, G. Crone, F. Cuttaia, O. D'Arcangelo, L. Danese, R. D. Davies, R. J. Davis, L. de Angelis, G. C. de Gasperis, L. de La Fuente, A. de Rosa, G. de Zotti, M. C. Falvella, F. Ferrari, R. Ferretti, L. Figini, S. Fogliani, C. Franceschet, E. Franceschi, T. Gaier, S. Garavaglia, F. Gomez, K. Gorski, A. Gregorio, P. Guzzi, J. M. Herreros, S. R. Hildebrandt, R. Hoyland, N. Hughes, M. Janssen, P. Jukkala, D. Kettle, V. H. Kilpi&auml;, M. Laaninen, P. M. Lapolla, C. R. Lawrence, D. Lawson, J. P. Leahy, R. Leonardi, P. Leutenegger, S. Levin, P. B. Lilje, S. R. Lowe, P. M. Lubin, D. Maino, M. Malaspina, M. Maris, J. Marti-Canales, E. Martinez-Gonzalez, A. Mediavilla, P. Meinhold, M. Miccolis, G. Morgante, P. Natoli, R. Nesti, L. Pagan, C. Paine, B. Partridge, J. P. Pascual, F. Pasian, D. Pearson, M. Pecora, F. Perrotta, P. Platania, M. Pospieszalski, T. Poutanen, M. Prina, R. Rebolo, N. Roddis, J. A. Rubi&ntilde;o-Martin, M. J. Salmon, M. Sandri, M. Seiffert, R. Silvestri, A. Simonetto, P. Sjoman, G. F. Smoot, C. Sozzi, L. Stringhetti, E. Taddei, J. Tauber, L. Terenzi, M. Tomasi, J. Tuovinen, L. Valenziano, J. Varis, N. Vittorio, L. A. Wade, A. Wilkinson, F. Winder, A. Zacchei, A. Zonca, A&A, '''520''', A4+, (2010).<br />
|lamarre2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12975-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The HFI instrument, from specification to actual performance'''], J.-M. Lamarre, J.-L. Puget, P. A. R. Ade, F. Bouchet, G. Guyot, A. E. Lange, F. Pajot, A. Arondel, K. Benabed, J.-L. Beney, A. Beno&icirc;t, J.-P. Bernard, R. Bhatia, Y. Blanc, J. J. Bock, E. Br&eacute;elle, T. W. Bradshaw, P. Camus, A. Catalano, J. Charra, M. Charra, S. E. Church, F. Couchot, A. Coulais, B. P. Crill, M. R. Crook, K. Dassas, P. de Bernardis, J. Delabrouille, P. de Marcillac, J.-M. Delouis, F.-X. D&eacute;sert, C. Dumesnil, X. Dupac, G. Efstathiou, P. Eng, C. Evesque, J.-J. Fourmond, K. Ganga, M. Giard, R. Gispert, L. Guglielmi, J. Haissinski, S. Henrot-Versill&eacute;, E. Hivon, W. A. Holmes, W. C. Jones, T. C. Koch, H. Lagard&egrave;re, P. Lami, J. Land&eacute;, B. Leriche, C. Leroy, Y. Longval, J. F. Mac&iacute;as-P&eacute;rez, T. Maciaszek, B. Maffei, B. Mansoux, C. Marty, S. Masi, C. Mercier, M.-A. Miville-Desch&ecirc;nes, A. Moneti, L. Montier, J. A. Murphy, J. Narbonne, M. Nexon, C. G. Paine, J. Pahn, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, R. Pons, N. Ponthieu, S. Prunet, D. Rambaud, G. Recouvreur, C. Renault, I. Ristorcelli, C. Rosset, D. Santos, G. Savini, G. Serra, P. Stassi, R. V. Sudiwala, J.-F. Sygnet, J. A. Tauber, J.-P. Torre, M. Tristram, L. Vibert, A. Woodcraft, V. Yurchenko, D. Yvon, A&A, '''520''', A9+, (2010).<br />
|leahy2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12855-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Expected LFI polarisation capability'''], J. P. Leahy, M. Bersanelli, O. D'Arcangelo, K. Ganga, S. M. Leach, A. Moss, E. Keih&auml;nen, R. Keskitalo, H. Kurki-Suonio, T. Poutanen, M. Sandri, D. Scott, J. Tauber, L. Valenziano, F. Villa, A. Wilkinson, A. Zonca, C. Baccigalupi, J. Borrill, R. C. Butler, F. Cuttaia, R. J. Davis, M. Frailis, E. Francheschi, S. Galeotta, A. Gregorio, R. Leonardi, N. Mandolesi, M. Maris, P. Meinhold, L. Mendes, A. Mennella, G. Morgante, G. Prezeau, G. Rocha, L. Stringhetti, L. Terenzi, M. Tomasi, A&A, '''520''', A8+, (2010).<br />
|maffei2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12999-09.pdf#page={{{2|1}}}'''Planck pre-launch status: HFI beam expectations from the optical optimisation of the focal plane'''], B. Maffei, F. Noviello, J. A. Murphy, P. A. R. Ade, J.-M. Lamarre, F. R. Bouchet, J. Brossard, A. Catalano, R. Colgan, R. Gispert, E. Gleeson, C. V. Haynes, W. C. Jones, A. E. Lange, Y. Longval, I. McAuley, F. Pajot, T. Peacocke, G. Pisano, J.-L. Puget, I. Ristorcelli, G. Savini, R. Sudiwala, R. J. Wylde, V. Yurchenko, A&A, '''520''', A12+, (2010).<br />
|mandolesi2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12837-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The Planck-LFI programme'''], N. Mandolesi, M. Bersanelli, R. C. Butler, E. Artal, C. Baccigalupi, A. Balbi, A. J. Banday, R. B. Barreiro, M. Bartelmann, K. Bennett, P. Bhandari, A. Bonaldi, J. Borrill, M. Bremer, C. Burigana, R. C. Bowman, P. Cabella, C. Cantalupo, B. Cappellini, T. Courvoisier, G. Crone, F. Cuttaia, L. Danese, O. D'Arcangelo, R. D. Davies, R. J. Davis, L. de Angelis, G. de Gasperis, A. de Rosa, G. de Troia, G. de Zotti, J. Dick, C. Dickinson, J. M. Diego, S. Donzelli, U. D&ouml;rl, X. Dupac, T. A. En&szlig;lin, H. K. Eriksen, M. C. Falvella, F. Finelli, M. Frailis, E. Franceschi, T. Gaier, S. Galeotta, F. Gasparo, G. Giardino, F. Gomez, J. Gonzalez-Nuevo, K. M. G&oacute;rski, A. Gregorio, A. Gruppuso, F. Hansen, R. Hell, D. Herranz, J. M. Herreros, S. Hildebrandt, W. Hovest, R. Hoyland, K. Huffenberger, M. Janssen, T. Jaffe, E. Keih&auml;nen, R. Keskitalo, T. Kisner, H. Kurki-Suonio, A. L&auml;hteenm&auml;ki, C. R. Lawrence, S. M. Leach, J. P. Leahy, R. Leonardi, S. Levin, P. B. Lilje, M. L&oacute;pez-Caniego, S. R. Lowe, P. M. Lubin, D. Maino, M. Malaspina, M. Maris, J. Marti-Canales, E. Martinez-Gonzalez, M. Massardi, S. Matarrese, F. Matthai, P. Meinhold, A. Melchiorri, L. Mendes, A. Mennella, G. Morgante, G. Morigi, N. Morisset, A. Moss, A. Nash, P. Natoli, R. Nesti, C. Paine, B. Partridge, F. Pasian, T. Passvogel, D. Pearson, L. P&eacute;rez-Cuevas, F. Perrotta, G. Polenta, L. A. Popa, T. Poutanen, G. Prezeau, M. Prina, J. P. Rachen, R. Rebolo, M. Reinecke, S. Ricciardi, T. Riller, G. Rocha, N. Roddis, R. Rohlfs, J. A. Rubi&ntilde;o-Martin, E. Salerno, M. Sandri, D. Scott, M. Seiffert, J. Silk, A. Simonetto, G. F. Smoot, C. Sozzi, J. Sternberg, F. Stivoli, L. Stringhetti, J. Tauber, L. Terenzi, M. Tomasi, J. Tuovinen, M. T&uuml;rler, L. Valenziano, J. Varis, P. Vielva, F. Villa, N. Vittorio, L. Wade, M. White, S. White, A. Wilkinson, A. Zacchei, A. Zonca, A&A, '''520''', A3+, (2010).<br />
|mennella2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12849-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Low Frequency Instrument calibration and expected scientific performance'''], A. Mennella, M. Bersanelli, R. C. Butler, F. Cuttaia, O. D'Arcangelo, R. J. Davis, M. Frailis, S. Galeotta, A. Gregorio, C. R. Lawrence, R. Leonardi, S. R. Lowe, N. Mandolesi, M. Maris, P. Meinhold, L. Mendes, G. Morgante, M. Sandri, L. Stringhetti, L. Terenzi, M. Tomasi, L. Valenziano, F. Villa, A. Zacchei, A. Zonca, M. Balasini, C. Franceschet, P. Battaglia, P. M. Lapolla, P. Leutenegger, M. Miccolis, L. Pagan, R. Silvestri, B. Aja, E. Artal, G. Baldan, P. Bastia, T. Bernardino, L. Boschini, G. Cafagna, B. Cappellini, F. Cavaliere, F. Colombo, L. de La Fuente, J. Edgeley, M. C. Falvella, F. Ferrari, S. Fogliani, E. Franceschi, T. Gaier, F. Gomez, J. M. Herreros, S. Hildebrandt, R. Hoyland, N. Hughes, P. Jukkala, D. Kettle, M. Laaninen, D. Lawson, P. Leahy, S. Levin, P. B. Lilje, D. Maino, M. Malaspina, P. Manzato, J. Marti-Canales, E. Martinez-Gonzalez, A. Mediavilla, F. Pasian, J. P. Pascual, M. Pecora, L. Peres-Cuevas, P. Platania, M. Pospieszalsky, T. Poutanen, R. Rebolo, N. Roddis, M. Salmon, M. Seiffert, A. Simonetto, C. Sozzi, J. Tauber, J. Tuovinen, J. Varis, A. Wilkinson, F. Winder, A&A, '''520''', A5+, (2010).<br />
|pajot2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13203-09.pdf#page={{{2|1}}}'''Planck pre-launch status: HFI ground calibration'''], F. Pajot, P. A. R. Ade, J.-L. Beney, E. Br&eacute;elle, D. Broszkiewicz, P. Camus, C. Carab&eacute;tian, A. Catalano, A. Chardin, M. Charra, J. Charra, R. Cizeron, F. Couchot, A. Coulais, B. P. Crill, K. Dassas, J. Daubin, P. de Bernardis, P. de Marcillac, J.-M. Delouis, F.-X. D&eacute;sert, P. Duret, P. Eng, C. Evesque, J.-J. Fourmond, S. Fran&ccedel;ois, M. Giard, Y. Giraud-H&eacute;raud, L. Guglielmi, G. Guyot, J. Haissinski, S. Henrot-Versill&eacute;, V. Hervier, W. Holmes, W. C. Jones, J.-M. Lamarre, P. Lami, A. E. Lange, M. Lefebvre, B. Leriche, C. Leroy, J. Macias-Perez, T. Maciaszek, B. Maffei, A. Mahendran, B. Mansoux, C. Marty, S. Masi, C. Mercier, M.-A. Miville-Deschenes, L. Montier, C. Nicolas, F. Noviello, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, R. Pons, N. Ponthieu, J.-L. Puget, D. Rambaud, C. Renault, J.-C. Renault, C. Rioux, I. Ristorcelli, C. Rosset, G. Savini, R. Sudiwala, J.-P. Torre, M. Tristram, D. Vall&eacute;e, M. Veneziani, D. Yvon, A&A, '''520''', A10+, (2010).<br />
|rosset2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa13054-09.pdf#page={{{2|1}}}'''Planck pre-launch status: High Frequency Instrument polarization calibration'''], C. Rosset, M. Tristram, N. Ponthieu, P. Ade, J. Aumont, A. Catalano, L. Conversi, F. Couchot, B. P. Crill, F.-X. D&eacute;sert, K. Ganga, M. Giard, Y. Giraud-H&eacute;raud, J. Ha&iuml;ssinski, S. Henrot-Versill&eacute;, W. Holmes, W. C. Jones, J.-M. Lamarre, A. Lange, C. Leroy, J. Mac&iacute;as-P&eacute;rez, B. Maffei, P. de Marcillac, M.-A. Miville-Desch&ecirc;nes, L. Montier, F. Noviello, F. Pajot, O. Perdereau, F. Piacentini, M. Piat, S. Plaszczynski, E. Pointecouteau, J.-L. Puget, I. Ristorcelli, G. Savini, R. Sudiwala, M. Veneziani, D. Yvon, A&A, '''520''', A13+, (2010).<br />
|sandri2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12891-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Low Frequency Instrument optics'''], M. Sandri, F. Villa, M. Bersanelli, C. Burigana, R. C. Butler, O. D'Arcangelo, L. Figini, A. Gregorio, C. R. Lawrence, D. Maino, N. Mandolesi, M. Maris, R. Nesti, F. Perrotta, P. Platania, A. Simonetto, C. Sozzi, J. Tauber, L. Valenziano, A&A, '''520''', A7+, (2010).<br />
|tauber2010a=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12983-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The Planck mission'''], J. A. Tauber, N. Mandolesi, J.-L. Puget, T. Banos, M. Bersanelli, F. R. Bouchet, R. C. Butler, J. Charra, G. Crone, J. Dodsworth, et al., A&A, '''520''', A1+, (2010).<br />
|tauber2010b=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12911-09.pdf#page={{{2|1}}}'''Planck pre-launch status: The optical system'''], J. A. Tauber, H. U. N&oslash;rgaard-Nielsen, P. A. R. Ade, J. Amiri Parian, T. Banos, M. Bersanelli, C. Burigana, A. Chamballu, D. de Chambure, P. R. Christensen, O. Corre, A. Cozzani, B. Crill, G. Crone, O. D'Arcangelo, R. Daddato, D. Doyle, D. Dubruel, G. Forma, R. Hills, K. Huffenberger, A. H. Jaffe, N. Jessen, P. Kletzkine, J. M. Lamarre, J. P. Leahy, Y. Longval, P. de Maagt, B. Maffei, N. Mandolesi, J. Mart&iacute;-Canales, A. Mart&iacute;n-Polegre, P. Martin, L. Mendes, J. A. Murphy, P. Nielsen, F. Noviello, M. Paquay, T. Peacocke, N. Ponthieu, K. Pontoppidan, I. Ristorcelli, J.-B. Riti, L. Rolo, C. Rosset, M. Sandri, G. Savini, R. Sudiwala, M. Tristram, L. Valenziano, M. van der Vorst, K. van't Klooster, F. Villa, V. Yurchenko, A&A, '''520''', A2+, (2010).<br />
|villa2010=[http://www.aanda.org/articles/aa/pdf/2010/12/aa12860-09.pdf#page={{{2|1}}}'''Planck pre-launch status: Calibration of the Low Frequency Instrument flight model radiometers'''], F. Villa, L. Terenzi, M. Sandri, P. Meinhold, T. Poutanen, P. Battaglia, C. Franceschet, N. Hughes, M. Laaninen, P. Lapolla, M. Bersanelli, R. C. Butler, F. Cuttaia, O. D'Arcangelo, M. Frailis, E. Franceschi, S. Galeotta, A. Gregorio, R. Leonardi, S. R. Lowe, N. Mandolesi, M. Maris, L. Mendes, A. Mennella, G. Morgante, L. Stringhetti, M. Tomasi, L. Valenziano, A. Zacchei, A. Zonca, B. Aja, E. Artal, M. Balasini, T. Bernardino, E. Blackhurst, L. Boschini, B. Cappellini, F. Cavaliere, A. Colin, F. Colombo, R. J. Davis, L. de La Fuente, J. Edgeley, T. Gaier, A. Galtress, R. Hoyland, P. Jukkala, D. Kettle, V.-H. Kilpia, C. R. Lawrence, D. Lawson, J. P. Leahy, P. Leutenegger, S. Levin, D. Maino, M. Malaspina, A. Mediavilla, M. Miccolis, L. Pagan, J. P. Pascual, F. Pasian, M. Pecora, M. Pospieszalski, N. Roddis, M. J. Salmon, M. Seiffert, R. Silvestri, A. Simonetto, P. Sjoman, C. Sozzi, J. Tuovinen, J. Varis, A. Wilkinson, F. Winder, A&A, '''520''', A6+, (2010).<br />
}}<br />
|name={{{1}}}}}</includeonly></div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=File:Pla_v3.zip&diff=11611File:Pla v3.zip2017-12-04T19:39:56Z<p>Mlopezca: File uploaded with MsUpload</p>
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<div>File uploaded with MsUpload</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Planets_related_data&diff=11610Planets related data2017-12-04T19:39:31Z<p>Mlopezca: Created page with "The planet flux density measurements are described in a paper that is available on the arXiv: https://arxiv.org/abs/1612.07151 and published in Astronomy and Astrophysics. Th..."</p>
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<div>The planet flux density measurements are described in a paper that is available on the arXiv: https://arxiv.org/abs/1612.07151 and published in Astronomy and Astrophysics.<br />
<br />
The raw data can be downloaded [[:File:pla_v3.zip|here]]. The interpretation of these data should become apparent from reading the paper. However, the following list aims to provide accurate descriptions of each of the parameters that we provide. For any questions, please contact the corresponding author, Jon E. Gudmundsson: jon.gudmundsson@fysik.su.se / jegudmunds@gmail.com<br />
<br />
The results are contained in 21 data files, one file for each planet observation made by Planck HFI. Each file contains 15 data columns. These data should allow the reader to reconstruct the Planck-HFI estimates of planet spectral radiance as well as associated uncertainties, both statistical and systematic.<br />
<br />
The columns are:<br />
* '''Channel''': The Planck-HFI detector name.<br />
* '''obs tims''': The observation time [MJD] corresponding to the time when this detector is centred on the planet.<br />
* '''RA''': Astrometric right ascension (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''DEC''': Astrometric declination (ICRF/J2000) ephmeris corresponding to the location of the planet at the time of observation. <BR>Observer location is set to: Planck Space Observatory [500@-489]<br />
* '''pda''': Solid angle [arcsec^2] of the planet used in the brightness analysis. <BR>This corresponds to $\Omega _\mathrm{p}$ in the paper (see discussion in Section 2.1). <br />
* '''dpda''': Assumed error in planet solid angle [arcsec^2].<br />
* '''T''': Planet thermodynamic temperature [K] (see definition in paper) at the band reference frequency <BR>corresponding to 100, 143, 217, 353, 545, or 857 GHz (see discussion in Section 2.1).<br />
* '''dT1''': Statistical uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''dT2''': Systematic uncertainty in the determination of the thermodynamic temperature [K].<br />
* '''flux''': Flux density [Jy] estimate at the reference frequency found by calculating the product of the planet <BR>solid angle and the Planck blackbody formula with the planet thermodynamic temperature and reference frequency as inputs.<br />
* '''dflux1''': Statistical uncertainty in flux density [Jy].<br />
* '''dflux2''': Systematic uncertainty in flux density [Jy].<br />
* '''beam''': Scanning beam solid angle [arcmin^2] used for this channel. <BR>This corresponds to $\Omega _\mathrm{b}$ in the paper (see discussion in Section 2.1).<br />
* '''kappa1''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _1$ in the paper (see definition in Section 2.1).<br />
* '''kappa2''': Colour correction coefficient appropriate for this detector and planet. <BR>This corresponds to $\kappa _2$ in the paper (see definition in Section 2.1).</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11609Main Page2017-12-04T19:37:41Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]]<br />
##[[Planets related data | Planet-related data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11608Main Page2017-12-04T19:34:35Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=Main_Page&diff=11607Main Page2017-12-04T19:33:14Z<p>Mlopezca: </p>
<hr />
<div>{{DISPLAYTITLE:Planck 2015 Release Explanatory Supplement}}<br />
<br />
<!---'''<span style="font-size:180%"> <span style="color:Blue"> This is the Explanatory Supplement development page for the Planck 2015 data release </span><br />
<br />
<br />
<br />
* Instructions for new users: [[Help:READ ME FIRST|Read me first]]<br />
* See [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for a detailed User Guide of the MediaWiki software;<br />
* See [[Help:Contents|Explanatory Supplement Help page]] for Planck-specific guidelines.---><br />
<br />
==Explanatory Supplement for the 2015 data release ==<br />
<br />
By the [[Planck Collaboration]]<br />
<br />
The Explanatory Supplement is a reference text accompanying the public data delivered from the operations of the European Space Agency’s Planck satellite during its mission.<br />
<!--- ############# ---><br />
#[[Introduction_WiP|Introduction]]<br />
##[[The Planck mission_WiP|The Planck mission]] <br />
##[[The satellite_WiP|The spacecraft]]<br />
##[[Ground Segment and Operations|Ground segment and operations]]<br />
##[[Survey_scanning_and_performance|Survey scanning and performance]]<br />
##[[Questions and Answers|Q&A]]<br />
<!--- ############# ---><br />
#[[The Instruments_WiP|The Instruments]]<br />
##[[HFI design, qualification, and performance|HFI design, qualification, and performance]]<br />
###[[HFI_cryogenics | Cryogenics]]<br />
###[[HFI_cold_optics | Cold optics]]<br />
###[[HFI_detection_chain | Detection chain]]<br />
###[[HFI_operations | Operations]]<br />
###[[HFI_performance_summary | Performance summary]]<br />
###[[HFI_instrument_annexes | Annexes]]<br />
##[[LFI overview|LFI design, qualification, and performance]]<span style="color:red"></span><br />
###[[LFI design, qualification, and performance#LFIDescription| Instrument description]]<br />
###[[LFI design, qualification, and performance#LFITests| Ground tests]]<br />
###[[LFI design, qualification, and performance#LFICalibration| In-flight calibration]]<br />
###[[LFI design, qualification, and performance#LFIPerformance| Performance summary]]<br />
###[[LFI design, qualification, and performance#LFISystematics| Systematic effects]]<br />
###[[LFI design, qualification, and performance#SCS| Sorption cooler]]<br />
###[[LFIAppendix| Annexes]]<br />
<!--- ############# ---><br />
#[[Data processing]]<br />
##[[The HFI DPC| HFI Data Processing]]<br />
###[[Pre-processing | Pre-processing]]<br />
###[[TOI processing|TOI processing]]<br />
###[[ADC correction]]<br />
###[[Beams | Beams]]<br />
###[[Map-making | Mapmaking]]<br />
###[[Spectral response | Spectral response]]<br />
###[[HFI-Validation | Internal overall validation]]<br />
<!--- ###[[Power spectra | Power spectra]] <span style="color:red">Not ready for release</span> ---><br />
###[[Summary_of_HFI_data_characteristics | Summary of HFI data characteristics]]<br />
##[[The LFI DPC| LFI data processing]] <span style="color:red"></span><br />
###[[Pre-processing_LFI| Pre-processing]]<br />
###[[TOI processing_LFI| TOI processing]] <span style="color:red"></span><br />
###[[Beams_LFI | Beams]] <span style="color:red"></span><br />
###[[Galactic stray light removal]]<br />
###[[Map-making_LFI | Mapmaking]] <span style="color:red"></span><br />
###[[LFI systematic effect uncertainties | Systematic effects uncertainties]]<br />
###[[LFI-Validation | Internal overall validation]] <span style="color:red"></span><br />
<!--- ###[[L3_LFI | Power spectra]] ---><br />
###[[Summary_LFI | Summary of LFI data characteristics ]]<br />
##[[HFI/LFI joint data processing]]<br />
###[[Detector pointing| Detector pointing]]<br />
<!--- ###[[NoiseCovarMatrices | Noise covariance matrices and low-resolution maps ]] ---><br />
###[[Compact Source catalogues| Compact Source Catalogues]]<br />
###[[Astrophysical component separation]]<br />
###[[C2 | CMB Power spectra and Planck likelihood code]]<br />
<!--- ############# ---><br />
#[[Mission products]]<br />
##[[Timelines | Time-ordered data]]<br />
##[[Frequency Maps | Sky temperature and polarization maps]]<br />
<!--- ##[[NoiseCovariance | Noise covariance matrices and low-resolution maps ]]<span style="color:red">(Keskitalo)</span ---><br />
##[[The RIMO|Instrument model]] <br />
##[[Planets related data | Planet-related data]] <br />
##[[Scanning Beams | Scanning Beams]]<br />
##[[Effective Beams | Effective beams]]<br />
##[[Catalogues|Catalogues]]: [[Catalogues#Catalogue of Compact Sources|PCCS]] • [[Catalogues#SZ Catalogue|PSZ]] • [[Catalogues#.282015.29_Planck_Catalogue_of_Galactic_Cold_Clumps|PGCC]] • [[Catalogues#.282015.29_Planck_List_of_high-redshift_source_candidates|PHZ]]<br />
##[[CMB_and_astrophysical_component maps | CMB and astrophysical component maps]]<br />
##[[CMB spectrum & Likelihood Code | CMB spectrum & Likelihood Code]]<br />
##[[Cosmological Parameters | Cosmological parameters and MC chains]]<br />
##[[Specially processed maps | Additional maps]]: <br />
###[[Specially processed maps#2015 Lensing map | 2015 Lensing map]] <br />
###[[Specially processed maps#2015 Compton parameter map | 2015 Compton parameter map]]<br />
###[[Specially_processed_maps#2015_Lensing-induced_B-mode_map | 2015 Lensing-induced B-mode map]]<br />
###[[Specially processed maps#2015 Integrated Sachs Wolfe effect maps | 2015 Integrated Sachs Wolfe effect map]]<br />
##[[Simulation data | Simulation data]] <br />
##[[DatesObs|Dates of observations]]<br />
<!--- ############# ---><br />
#[[Software utilities|Software utilities]]<br />
##[[Unit conversion and Color correction|Unit conversion and Colour correction]] <br />
<!--- ############# ---><br />
#[[Operational data]]<br />
<!---##[[Thermal|Thermal and cooler system]]---><br />
##[[Survey history | Survey history data]]<br />
##[[Planck operational state history]]<br />
<!---##[[FOG|Fibre-optic gyro]]---><br />
##[[SREM|Space radiation environment monitor]]<br />
#[[Appendix]]<br />
##[[Glossary]]<br />
##[[List of acronyms]]<br />
[[Category:PSOBook]]</div>Mlopezcahttps://wiki.cosmos.esa.int/planckpla2015/index.php?title=CMB_and_astrophysical_component_maps&diff=11606CMB and astrophysical component maps2017-09-12T06:53:15Z<p>Mlopezca: /* Production process */</p>
<hr />
<div>{{DISPLAYTITLE:2015 CMB and astrophysical component maps}}<br />
<br />
== Overview ==<br />
This section describes the maps of astrophysical components produced from the Planck data. These products are derived from some or all of the nine frequency channel maps described above using different techniques and, in some cases, using other constraints from external data sets. Here we give a brief description of each product and how it is obtained, followed by a description of the FITS file containing the data and associated information.<br />
All the details can be found in {{PlanckPapers|planck2014-a11}} and {{PlanckPapers|planck2014-a12}}.<br />
<br />
==CMB maps==<br />
CMB maps have been produced using four different methods: COMMANDER, NILC, SEVEM, and SMICA, as described in the [[Astrophysical_component_separation#CMB_and_foreground_separation | CMB and foreground separation]] section and also in Appendices A-D of {{PlanckPapers|planck2014-a11}} and references therein.<br />
<br />
'''As discussed extensively in {{PlanckPapers|planck2014-a01}}, {{PlanckPapers|planck2014-a07}}, {{PlanckPapers|planck2014-a09}}, and {{PlanckPapers|planck2014-a11}}, the residual systematics in the Planck 2015 polarization maps have been dramatically reduced compared to 2013, by as much as two orders of magnitude on large angular scales. Nevertheless, on angular scales greater than 10 degrees, correponding to l < 20, systematics are still non-negligible compared to the expected cosmological signal.'''<br />
<br />
'''It was not possible, for this data release, to fully characterize the large-scale residuals from the data or from simulations. Therefore all results published by the Planck Collaboration in 2015 which are based on CMB polarization have used maps which have been high-pass filtered to remove the large angular scales. We warn all users of the CMB polarization maps that they cannot yet be used for cosmological studies at large angular scales.'''<br />
<br />
'''For convenience, we provide as default polarized CMB maps from which all angular scales at l < 30 have been filtered out. '''<br />
<br />
For each method we provide the following:<br />
* Full-mission CMB intensity map, confidence mask and beam transfer function.<br />
* Full-mission CMB polarisation map, <br />
* A confidence mask.<br />
* A beam transfer function.<br />
In addition, and for characterisation purposes, we include six other sets of maps from three data splits: first/second half-ring, odd/even years and first/second half-mission. For the year-1,2 and half-mission-1,2 data splits we provide half-sum and half-difference maps which are produced by running the corresponding sums and differences inputs through the pipelines. The half-difference maps can be used to provide an approximate noise estimates for the full mission, but they should be used with caution. Each split has caveats in this regard: there are noise correlations between the half-ring maps, and missing pixels in the other splits. The Intensity maps are provided at Nside = 2048, at 5 arcmin resolution, while the Polarisation ones are provided at Nside = 1024, at 10 arcmin resolution. All maps are in units of K<sub>cmb</sub>.<br />
<br />
In addition, for each method we provide three sets of files, each categorized by the "R2.0X" label as follows:<br />
<br />
; ''R2.02''<br />
<pre style="white-space: pre-wrap; <br />
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white-space: -o-pre-wrap; <br />
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This set of intensity and polarisation maps are provided at a resolution of Nside=1024. The Stokes Q and U maps are high-pass filtered to contain only modes above l > 30, as explained above and as used for analysis by the Planck Collaboration; THESE ARE THE POLARISATION MAPS WHICH SHOULD BE USED FOR COSMOLOGICAL ANALYSIS. Each type of map is packaged into a separate fits file (as for "R2.01"), resulting in file sizes which are easier to download (as opposed to the "R2.00" files), and more convenient to use with commonly used analysis software.<br />
</pre><br />
<br />
; ''R2.01''<br />
<pre style="white-space: pre-wrap; <br />
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This is the most complete set of 2015 CMB maps, containing Intensity products at a resolution of Nside=2048, and both Intensity and Polarisation at resolution of Nside=1024. For polarisation (Q and U), they contain all angular resolution modes. WE CAUTION USERS ONCE AGAIN THAT THE STOKES Q AND U MAPS ARE NOT CONSIDERED USEABLE FOR COSMOLOGICAL ANALYSIS AT l < 30. The structure of these files is the same as for "R2.02".<br />
</pre><br />
<br />
; ''R2.00''<br />
<pre style="white-space: pre-wrap; <br />
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white-space: -o-pre-wrap; <br />
word-wrap: break-word;"><br />
This set of files is equivalent to the "R2.01" set, but are packaged into only two large files. Warning: downloading these files could be very lengthy...<br />
</pre><br />
<br />
For a complete description of the above data structures, see [[#File names and structure | below]]; the content of the first extensions is illustrated and commented in the table below.<br />
<br />
<br />
The gallery below shows the Intensity, noise from half-mission, half-difference, and confidence mask for the four pipelines, in the order COMMANDER, NILC, SEVEM and SMICA, from top to bottom. The Intensity maps' scale is [–500.+500] μK, and the noise spans [–25,+25] μK. We do not show the Q and U maps since they have no significant visible structure to contemplate.<br />
<br />
<center><br />
<gallery style="padding:0 0 0 0;" perrow=3 widths=300px heights=180> <br />
File:CMB_commander_tsig.png | '''commander temperature'''<br />
File:CMB_commander_tnoi.png | '''commander noise'''<br />
File:CMB_commander_tmask.png | '''commander mask'''<br />
File:CMB_nilc_tsig.png | '''nilc temperature'''<br />
File:CMB_nilc_tnoi.png | '''nilc noise'''<br />
File:CMB_nilc_tmask.png | '''nilc mask'''<br />
File:CMB_sevem_tsig.png | '''sevem temperature'''<br />
File:CMB_sevem_tnoi.png | '''sevem noise'''<br />
File:CMB_sevem_tmask.png | '''sevem mask'''<br />
File:CMB_smica_tsig.png | '''smica temperature'''<br />
File:CMB_smica_tnoi.png | '''smica noise'''<br />
File:CMB_smica_tmask.png | '''smica mask'''</gallery><br />
</center><br />
<br />
===Product description ===<br />
<br />
====COMMANDER====<br />
<br />
;Principle<br />
<br />
: COMMANDER is a Planck software code implementing pixel based Bayesian parametric component separation. Each astrophysical signal component is modelled in terms of a small number of free parameters per pixel, typically in terms of an amplitude at a given reference frequency and a small set of spectral parameters, and these are fitted to the data with an MCMC Gibbs sampling algorithm. Instrumental parameters, including calibration, bandpass corrections, monopole and dipoles, are fitted jointly with the astrophysical components. A new feature in the Planck 2015 analysis is that the astrophysical model is derived from a combination of Planck, WMAP and a 408 MHz (Haslam et al. 1982) survey, providing sufficient frequency support to resolve the low-frequency components into synchrotron, free-free and spinning dust. For full details, see {{PlanckPapers|planck2014-a12}}.<br />
<br />
; Resolution (effective beam)<br />
<br />
: The Commander sky maps have different angular resolutions depending on data products:<br />
* The components of the full astrophysical sky model derived from the complete data combination (Planck, WMAP, 408 MHz) have a 1 degree FWHM resolution, and are pixelized at N<sub>side</sub>=256. The corresponding CMB map defines the input map for the low-l Planck 2015 temperature likelihood. <br />
* The Commander CMB temperature map derived from Planck-only observations has an angular resolution of ~5 arcmin and is pixelized at N<sub>side</sub>=2048. This map is produced by harmonic space hybridiziation, in which independent solutions derived at 40 arcmin (using 30-857 GHz data), 7.5 arcmin (using 143-857 GHz data), and 5 arcmin (using 217-857 GHz data) are coadded into a single map.<br />
* The Commander CMB polarization map has an angular resolution of 10 arcmin and is pixelized at N<sub>side</sub>=1024. As for the temperature case, this map is produced by harmonic space hybridiziation, in which independent solutions derived at 40 arcmin (using 30-353 GHz data) and 10 arcmin (using 100-353 GHz data) are coadded into a single map.<br />
<br />
; Confidence mask<br />
<br />
: The Commander confidence masks are produced by thresholding the chi-square map characterizing the global fits, combined with direct CO amplitude thresholding to eliminate known leakage effects. In addition, we exclude the 9-year WMAP point source mask in the temperature mask. For full details, see Sections 5 and 6 in {{PlanckPapers|planck2014-a12}}. A total of 81% of the sky is admitted for high-resolution temperature analysis, and 83% for polarization analysis. For low-resolution temperature analysis, for which the additional WMAP and 408 MHz observations improve foreground constraints, a total of 93% of the sky is admitted. <br />
<br />
====NILC====<br />
<br />
;Principle<br />
<br />
: The Needlet-ILC (hereafter NILC) CMB map is constructed both in total intensity as well as polarization: Q and U Stokes parameters. For total intensity, all Planck frequency channels are included. For polarization, all polarization sensitive frequency channels are included, from 30 to 353 GHz. The solution, for T, Q and U is obtained by applying the Internal Linear Combination (ILC) technique in needlet space, that is, with combination weights which are allowed to vary over the sky and over the whole multipole range. <br />
<br />
; Resolution (effective beam)<br />
<br />
: The spectral analysis, and estimation of the NILC coefficients, is performed up to a maximum <math>\ell=4000</math>. The effective beam is equivalent to a Gaussian circular beam with FWHM=5 arcminutes. <br />
<br />
; Confidence mask<br />
<br />
: The same procedure is followed by SMICA and NILC for producing confidence masks, though with different parametrizations. A low resolution smoothed version of the NILC map, noise subtracted, is thresholded to 73.5 squared micro-K for T, and 6.75 squared micro-K for Q and U.<br />
<br />
<br />
====SEVEM====<br />
; Principle<br />
<br />
: SEVEM produces clean CMB maps at several frequencies by using a procedure based on template fitting in real space. The templates are typically constructed from the lowest and highest Planck frequencies and then subtracted from the CMB-dominated channels, with coefficients that are chosen to minimize the variance of the clean map outside a considered mask. In the cleaning process, no assumptions about the foregrounds or noise levels are needed, rendering the technique very robust. Two single frequency clean maps are then combined to obtain the final CMB map.<br />
<br />
;Resolution<br />
<br />
: For intensity the clean CMB map is constructed up to a maximum <math>\ell=4000</math> at Nside=2048 and at the standard resolution of 5 arcminutes (Gaussian beam).<br />
: For polarization the clean CMB map is produced at Nside=1024 with a resolution of 10 arcminutes (Gaussian beam) and a maximum <math>\ell=3071</math>.<br />
<br />
; Confidence masks<br />
<br />
: The confidence masks cover the most contaminated regions of the sky, leaving approximately 85 per cent of useful sky for intensity, and 80 per cent for polarization.<br />
<br />
=====Foregrounds-subtracted maps=====<br />
<br />
In addition to the regular CMB maps, SEVEM provides maps cleaned of the foregrounds for selected frequency channels (categorized as fgsub-sevem in the archive). In particular, for intensity there are clean CMB maps available at 100, 143 and 217 GHz, provided at the original resolution of the uncleaned channel and at Nside=2048. For polarization, there are Q/U clean CMB maps for the 70, 100 and 143 GHz (at Nside=1024). The 70 GHz clean map is provided at its original resolution, whereas the 100 and 143 GHz maps have a resolution given by a Gaussian beam with fwhm=10 arcminutes.<br />
<br />
====SMICA====<br />
; Principle<br />
: SMICA produces CMB maps by linearly combining all Planck input channels with multipole-dependent weights. It includes multipoles up to <math>\ell = 4000</math>. Temperature and polarization maps are produced independently.<br />
; Resolution (effective beam)<br />
: The SMICA intensity map has an effective beam window function of 5 arc-minutes which is truncated at <math>\ell=4000</math> and is '''not''' deconvolved from the pixel window function. Thus the delivered beam window function is the product of a Gaussian beam at 5 arcminutes and the pixel window function for <math>N_{side}</math>=2048.<br />
: The SMICA Q and U maps are obtained similarly but are produced at <math>N_{side}</math>=1024 with an effective beam of 10 arc-minutes (to be multiplied by the pixel window function, as for the intensity map).<br />
; Confidence mask<br />
: A confidence mask is provided which excludes some parts of the Galactic plane, some very bright areas and masked point sources. This mask provides a qualitative (and subjective) indication of the cleanliness of a pixel. See section below detailing the production process.<br />
<br />
<br />
==== Common Masks====<br />
<br />
A number of common masks have been defined for analysis of the CMB temperature and polarization maps. They are based on the confidence masks provided by the component separation methods. One mask for temperature and one mask for polarization have been chosen as the preferred masks based on subsequent analyses.<br />
<br />
The common masks for the CMB temperature maps are:<br />
<br />
* UT78: union of the Commander, SEVEM, and SMICA temperature confidence masks (the NILC mask was not included since it masks much less of the sky). It has f<sub>sky</sub> = 77.6%. This is the preferred mask for temperature.<br />
<br />
* UTA76: in addition to the UT78 mask, it masks pixels where standard deviation between the four CMB maps is greater than 10 &mu;K. It has f<sub>sky</sub> = 76.1%.<br />
<br />
The common masks for the CMB polarization maps are:<br />
<br />
* UP78: the union of the Commander, SEVEM and SMICA polarization confidence masks (the NILC mask was not included since it masks much less of the sky). It has f<sub>sky</sub> = 77.6%.<br />
<br />
* UPA77: In addition to the UP78 mask, it masks pixels where the standard deviation between the four CMB maps, averaged in Q and U, is greater than 4 &mu;K. It has f<sub>sky</sub> = 76.7%.<br />
<br />
* UPB77: in addition to the UP78 mask, it masks polarized point sources detected in the frequency channel maps. It has f<sub>sky</sub> = 77.4%. This is the preferred mask for polarization.<br />
<br />
====CMB-subtracted frequency maps ("Foreground maps")====<br />
<br />
These are the full-sky, full-mission frequency maps in intensity from which the CMB has been subtracted. The maps contain foregrounds and noise. They are provided for each frequency channel and for each component separation method. They are grouped into 8 files, two for each method of which there is one for each instrument. The maps are are at N<sub>side</sub> = 1024 for the three LFI channels and at N<sub>side</sub> = 2048 for the six HFI channels. The filenames are:<br />
<br />
* ''LFI_Foregrounds-{method}_1024_Rn.nn.fits'' (145 MB each)<br />
* ''HFI_Foregrounds-{method}_2048_Rn.nn.fits'' (1.2 GB each)<br />
<br />
To remove the CMB, the respective CMB map was first deconvolved with the 5 arcmin beam, then convolved with the beam of the frequency channel, and finally subtracted from the frequency map. This was done using the <math>B_\rm{l}</math> in harmonic space, assuming a symmetric beam.<br />
<br />
The CMB-subtracted maps have complicated noise properties. The CMB maps contain a noise contribution from each of the frequency maps, depending on the weights with which they were combined. Therefore subtracting the CMB map from a frequency channel contributes additional noise from the other frequency channels.<br />
<br />
The frequency maps from which the CMB have been subtracted are:<br />
<br />
* ''LFI_SkyMap_0nn_1024_R2.01_full.fits''<br />
* ''HFI_SkyMap_nnn_2048_R2.0n_full.fits''<br />
<br />
Note that the temperature column in the HFI R2.00, R2.01 and R2.02 is the same, since the changes in these maps involved the polarization columns only. Also note that the zodiacal light correction described [https://wiki.cosmos.esa.int/planckpla2015/index.php/Map-making#Zodiacal_light_correction here] was applied to the HFI temperature maps before the CMB subtraction.<br />
<br />
====Quadrupole Residual Maps====<br />
<br />
The second-order (kinematic) quadrupole is a frequency-dependent effect. During the production of the frequency maps the frequency-independent part was subtracted, which leaves a frequency-dependent residual quadrupole. The residuals in the component-separated CMB temperature maps have been estimated by simulating the effect in the frequency maps and propagating it through the component separation pipelines. The residuals have an amplitude of around 2 &mu;K peak-to-peak. The maps of the estimated residuals can be used to remove the effect by subtracting them from the CMB maps.<br />
<br />
===Production process===<br />
<br />
====COMMANDER====<br />
<br />
; Pre-processing<br />
<br />
: All sky maps are first convolved to a common resolution that is larger than the largest beam of any frequency channel. For the combined Planck, WMAP and 408 MHz temperature analysis, the common resolution is 1 degree FWHM; for the Planck-only, all-frequency analysis it is 40 arcmin FWHM; and for the intermediate-resolution analysis it is 7.5 arcmin; while for the full-resolution analysis, we assume all frequencies between 217 and 857 GHz have a common resolution, and no additional convolution is performed. For polarization, only two smoothing scales are employed, 40 and 10 arcmin, respectively. The instrumental noise rms maps are convolved correspondingly, properly accouting for their matrix-like nature. <br />
<br />
; Priors<br />
<br />
: The following priors are enforced in the Commander analysis:<br />
* All foreground amplitudes are enforced to be positive definite in the low-resolution analysis, while no amplitude priors are enforced in the high-resolution analyses<br />
* Monopoles and dipoles are fixed to nominal values for a small set of reference frequencies<br />
* Gaussian priors are enforced on spectral parameters, with values informed by the values derived in the high signal-to-noise areas of the sky<br />
* The Jeffreys ignorance prior is enforced on spectral parameters in addition to the informative Gaussian priors<br />
<br />
; Fitting procedure<br />
<br />
: Given data and priors, Commander either maximizes, or samples from, the Bayesian posterior, P(theta|data). Because this is a highly non-Gaussian and correlated distribution, involving millions of parameters, these operations are performed by means of the Gibbs sampling algorithm, in which joint samples from the full distributions are generated by iteratively sampling from the corresponding conditional posterior distributions, P(theta_i| data, theta_{j/=i}). For the low-resolution analysis, all parameters are optimized jointly, while in the high-resolution analyses, which employs fewer frequency channels, low signal-to-noise parameters are fixed to those derived at low resolution. Examples of such parameters include monopoles and dipoles, calibration and bandpass parameters, thermal dust temperature etc.<br />
<br />
====NILC====<br />
<br />
; Pre-processing<br />
<br />
: All sky frequency maps are deconvolved using the DPC beam transfer function provided, and re-convolved with a 5 arcminutes FWHM circular Gaussian beam. In polarization, prior to the smoothing process, all sky E and B maps are derived from Q and U using standard HEALPix tools from each individual frequency channels <br />
<br />
; Linear combination<br />
<br />
: Pre-processed input frequency maps are decomposed in needlet coefficients, specified in the Appendix B of the Planck A11 paper, with shape given by Table B.1. Minimum variance coefficients are then obtained, using all channels for T, from 30 to 353 for E and B. <br />
<br />
; Post-processing<br />
<br />
: E and B maps are re-combined into Q and U products using standard HEALPix tools. <br />
<br />
====SEVEM====<br />
<br />
The templates used in the SEVEM pipeline are typically constructed by subtracting two close Planck frequency channel maps, after first smoothing them to a common resolution to ensure that the CMB signal is properly removed. A linear combination of the templates <math>t_j</math> is then subtracted from (hitherto unused) map d to produce a clean CMB map at that frequency. This is done in real space at each position on the sky: <math> T_c(\mathbf{x}, ν) = d(\mathbf{x}, ν) − \sum_{j=1}^{n_t} α_j t(\mathbf{x}) </math><br />
where <math>n_t</math> is the number of templates. The <math>α_j</math> coefficients are obtained by minimising the variance of the clean map <math>T_c</math> outside a given mask. Note that the same expression applies for I, Q and U. Although we exclude very contaminated regions during the minimization, the subtraction is performed for all pixels and, therefore, the cleaned maps cover the full-sky (although we expect that foreground residuals are present in the excluded areas).<br />
<br />
There are several possible configurations of SEVEM with regard to the number of frequency maps which are cleaned or the number of templates that are used in the fitting. Note that the production of clean maps at different frequencies is of great interest in order to test the robustness of the results, and