2015 CMB spectra and likelihood code

2015 CMB spectra

General description

TT

The Planck best-fit CMB temperature power spectrum, shown in the figure below, covers the wide range of multipoles ℓ = 2-2508. Over the multipole range ℓ = 2-29, the power spectrum is derived from the "Commander" component-separation algorithm applied to the combination of Planck 2015 temperature data between 30 and 857 GHz, the 9-year WMAP sky maps, and the 408 MHz Haslam et al. (1982) survey, including 93% of the sky (Planck-2015-A10). The asymmetric error bars associated with this spectrum are the 68% confidence limits and include the uncertainties due to foreground subtraction.

For multipoles equal or greater than ℓ = 30, instead, the spectrum is derived from the "Plik" likelihood Planck-2015-A11 by optimally combining the spectra in the frequency range 100-217 GHz, and correcting them for unresolved foregrounds using the best-fit foreground solution from a Planck TT+lowP ΛCDM run. Associated 1σ errors include beam uncertainties. Both Commander and Plik are described in more detail in the sections below.

Planck 2015 TT power spectrum. The x-axis is logarithmic up to ℓ = 30 and linear at higher ℓ . The red line is the Planck best-fit primordial power spectrum (cf. Planck TT+lowP in table 3 of Planck-2015-A15[3]). Residuals with respect to this model are shown in the lower panel. The error bars show ±1σ uncertainties.

TE and EE

The Planck best-fit CMB polarization and temperature-polarization cross-correlation power spectra, shown in the figure below, cover the multipole range ℓ = 2-1996. The data points relative to the multipole range ℓ = 2-29 are quadratic maximum likelihood estimates from foreground-cleaned Planck 70-GHz Q and U Stokes parameter maps using 46% of the sky (the same maps that are used in the "lowP" likelihood, see Planck-2015-A11). In the range ℓ = 2-29, we also release the BB, TB, and EB power spectra derived from the same maps (for the cross-spectra involving temperature, the Commander map is always used). Symmetric error bars are given as the 68% confidence intervals as derived from the Fisher information matrix of the estimates. Analogously to the TT case, the ℓ ≥ 30 spectrum is derived from the Plik likelihood Planck-2015-A11 by optimally combining the spectra in the frequency range 100-217 GHz, and correcting them for unresolved foregrounds using the best-fit foreground solution from a Planck TT,TE,EE+lowP ΛCDM run.

Planck 2015 EE power spectrum. The red line is the Planck best-fit primordial power spectrum (cf. Planck TT+lowP in table 3 of Planck-2015-A13). Residuals with respect to this model are shown in the lower panel. The error bars show ±1σ uncertainties. The green line in the lower panel shows the best-fit temperature-to-polarization leakage model of equations (11a) and (11b) in Planck-2015-A13.

Planck 2015 TE power spectrum. The red line is the Planck best-fit primordial power spectrum (cf. Planck TT+lowP in table 3 of Planck-2015-A13). Residuals with respect to this model are shown in the lower panel. The error bars show ±1σ uncertainties. The green line in the lower panel shows the best-fit temperature-to-polarization leakage model of equations (11a) and (11b) in Planck-2015-A13.

Production process

The ℓ < 30 part of the Planck TT power spectrum is derived from the Commander approach, which implements Bayesian component separation in pixel space, fitting a parametric model to the data by sampling the posterior distribution for the model parameters (Planck-2015-A10). The power spectrum at any multipole ℓ is given as the maximum probability point for the posterior C_ℓ distribution, marginalized over the other multipoles, and the error bars are from the 68% confidence level; see Planck-2015-A10.

The ℓ ≥ 30 part of the TT, TE, and EE power spectra have been derived by the Plik likelihood, a code that implements a pseudo-C_ℓ based technique, extensively described in section 2 and the appendix of Planck-2013-XV and Planck-2015-A11. Frequency spectra are computed as cross-spectra between half-mission maps. Mask and multipole range choices for each frequency spectrum are summarized in section 3.3 of Planck-2015-A13 and in Planck-2015-A11. The final power spectrum is an optimal combination of the 100, 143, 143×217, and 217-GHz spectra, corrected for the best-fit unresolved foregrounds and inter-frequency calibration factors, as derived from the full likelihood analysis (for TT we use the best-fit solutions for the nuisance parameters from the Planck+TT+lowP data combination, while for TE and EE we use the best fit from Planck+TT+lowP, cf. table 3 of Planck-2015-A13). A thorough description of the models of unresolved foregrounds is given in Planck-2015-A11. The spectrum covariance matrix accounts for cosmic variance and noise contributions, together with beam uncertainties. The ℓ≥30 CMB TT spectrum and associated covariance matrix are available in two formats.

1. Unbinned: TT, 2479 bandpowers (ℓ=30-2508); TE or EE, 1697 bandpowers (ℓ=30-1996).
2. Binned, in bins of Δℓ=30: TT, 83 bandpowers; TE or EE, 66 bandpowers.

We bin the C_ℓ power spectrum with a weight proportional to ℓ(ℓ+1), so that the C_ℓb binned bandpower centred on ℓ_b is [math]\\ C_{\ell_b}=\Sigma_{\ell \in b} w_{\ell_b\ell} C_\ell, \quad \text{with} \quad w_{\ell_b\ell}=\frac{\ell (\ell+1)}{\Sigma_{\ell \in b} \ell (\ell+1)}.\\[/math] Equivalently, using the matrix formalism, we can construct the binning matrix B as [math]\\ B_{\ell_b \ell}=w_{\ell_b\ell}, \\ [/math] where B is an [math] n_b\times n_\ell[/math] matrix, with n_b=83 being the number of bins and n_ℓ=2479 the number of unbinned multipoles. Thus [math] \\ {\bf C}_\mathrm{binned}=B \, {\bf C}, \\ \mathrm{cov_\mathrm{binned}}= B\, \mathrm{cov}\, B^{\rm T}, \\ \ell_b=B\, \ell .\\ [/math] Here, [math] {\bf C}_{\rm binned}\, ({\bf C})[/math] is the vector containing all the binned (unbinned) C_ℓ bandpowers, [math]\mathrm{cov}[/math] is the covariance matrix, and ℓ_b is the weighted average multipole in each bin. Note that following this definition, ℓ_b can be a non-integer. The binned D_ℓb power spectrum is then calculated as [math] \\ D_{\ell_b}=\frac{\ell_b (\ell_b+1)}{2\pi} C_{\ell_b}. [/math]

Inputs

Low-ℓ spectrum (ℓ<30):

• Planck 30 and 44 GHz frequency maps;
• Planck 70 to 857 GHz detector and detector-set maps;
• 9-year WMAP temperature sky maps between 23 and 94 GHz;
• 408 MHz survey of Haslam et al. (1982);
• Commander χ²-based LM93 confidence mask (Planck-2015-A10).

High-ℓ spectrum (30≤ℓ≤2508):

• 100, 143, 143×217 and 217 GHz spectra and their covariance matrix (setion 3.3 of Planck-2015-A13);
• best-fit foreground templates and inter-frequency calibration factors (table 3 of Planck-2015-A13);
• beam transfer function uncertainties (Planck-2015-A07).

File names and meta-data

The CMB spectrum and its covariance matrix are distributed in a single FITS file named

• COM_PowerSpect_CMB_R2.nn.fits,

which contains 7 BINTABLE extensions.

1. TT low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

2. TE low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

3. EE low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

4. TB low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

5. EB low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

6. BB low-ℓ, unbinned (TTLOLUNB)

with the low-ℓ part of the spectrum, not binned, and for ℓ=2-29. The table columns are:

1. ELL (integer), multipole number;
2. D_ELL (float), D_ℓ as described above;
3. ERRUP (float), the upward uncertainty;
4. ERRDOWN (float), the downward uncertainty.

7. TT high-ℓ, binned (TTHILBIN)

with the high-ℓ part of the spectrum, binned into 83 bins covering 〈ℓ〉= 47-2499 in bins of width ℓ=30 (with the exception of the last bin that is smaller). The table columns are:

1. ELL (float), mean multipole number of bin;
2. L_MIN (integer), lowest multipole of bin;
3. L_MAX (integer), highest multipole of bin;
4. D_ELL (float), D_ℓ as described above;
5. ERR (float), the uncertainty.

8. TT high-ℓ unbinned (TTHILUNB)

with the high-ℓ part of the spectrum, unbinned, in 2979 bins covering 〈ℓ〉= 30-2508. The table columns are:

1. ELL (integer), multipole;
2. D_ELL (float), D_ℓ as described above;
3. ERR (float), the uncertainty.

9. TE high-ℓ, binned (TEHILBIN)

with the high-ℓ part of the spectrum, binned into 83 bins covering 〈ℓ〉= 47-1988 in bins of width ℓ=30 (with the exception of the last bin that is smaller). The table columns are:

1. ELL (float), mean multipole number of bin;
2. L_MIN (integer), lowest multipole of bin;
3. L_MAX (integer), highest multipole of bin;
4. D_ELL (float), D_ℓ as described above;
5. ERR (float), the uncertainty.

10. TE high-ℓ, unbinned (TEHILUNB)

with the high-ℓ part of the spectrum, unbinned, in 2979 bins covering 〈ℓ〉= 30-1996. The table columns are:

1. ELL (integer), multipole;
2. D_ELL (float), D_ℓ as described above;
3. ERR (float), the uncertainty.

11. EE high-ℓ, binned (EEHILBIN)

with the high-ℓ part of the spectrum, binned into 83 bins covering 〈ℓ〉= 47-1988 in bins of width ℓ=30 (with the exception of the last bin that is smaller). The table columns are:

1. ELL (float), mean multipole number of bin;
2. L_MIN (integer), lowest multipole of bin;
3. L_MAX (integer), highest multipole of bin;
4. D_ELL (float), D_ℓ as described above;
5. ERR (float), the uncertainty.

12. EE high-ℓ, unbinned (EEHILUNB)

with the high-ℓ part of the spectrum, unbinned, in 2979 bins covering 〈ℓ〉= 30-1996. The table columns are:

1. ELL (integer), multipole;
2. D_ELL (float), D_ℓ as described above;
3. ERR (float), the uncertainty.

Note that R2.00 of these files contained a small error in that the effective ℓ of the bin in the binned data was truncated to an integer. While the ℓ values of the unbinned (full) data are indeed integers, the effective ℓ of the binned data is a weighted average of the ℓs used in a particular bin and should be a real number. This is corrected in R2.01.

The spectra give D_ℓ = ℓ(ℓ+1)C_ℓ / 2π in units of μK². The covariance matrices of the spectra will be released at a later time.

The CMB spectra are also given in seven simple text files, corresponding to each of the FITS file BINTABLE extensions described above.

Likelihood

The 2015 baseline likelihood release consists of a code package and a single data package. Four extended data packages are also available enabling exploration of alternatives to the baseline results. The code compiles to a library allowing for the computation of log likelihoods for a given data set. Each data package contains multiple data sets. A data set permits the computation of a single likelihood among:

• the high-ℓ temperature and polarization CMB (jointly or separately);
• the low-ℓ temperature and polarization CMB (jointly or separately);
• the CMB lensing reconstruction.

By combining the results from different data sets (possibly from different data packages), one can compute the likelihood of different subsets of the Planck data.

Only the baseline data package, COM_Likelihood_Data-baseline_R2.00.tar.gz, will be fully described on this page. It contains six data sets, which are enough to compute all of the baseline Planck results that are discussed in Planck-2015-A12. In particular it allows for the computation of the CMB and lensing likelihood from either the Temperature data only, or the Temperature + Polarization combination.

The other data packages contain data sets that extend the baseline results, enabling the exploration of different regimes, which are discussed in Planck-2015-A11 and Planck-2015-A15. Their full description is contained in the documentation included in each of the extended data packages.

Library and tools

Description

The library consists of code written in C and Fortran 90. It can be called from both of those languages. Optionally, a python wrapper can be built as well. Scripts to simplify the linking of the library with other codes are part of the package, as well as some example codes that can be used to test the correct installation of the code and the integrity of the data packages. Optionally, a script is also available, allowing the user to modify the multipole range of the TT likelihoods and reproduce the hybridization test performed in the paper. A description of the tool, the API of the library, as well as different installation procedure are detailed in the readme.md file in the code package.

File name

The code can be extracted from COM_Likelihood_Code-v2.0.R2.00.tar.bz2.

Please read the file readme.md for installation instructions.

Data sets - Baseline data

All of the released baseline data are distributed within a single file, COM_Likelihood_Data-baseline_R2.00.tar.gz.

This files extract to a directory hierarchy, containing the different data sets needed to compute different likelihoods.

Each data set is stored in its own directory. The directory structure follows similar rules for each data set, and stores data, template, and meta-data for each particular likelihood.

As in 2013, the CMB likelihood is cut into low-ℓ and high-ℓ parts. Moreover, for each of those, we distribute both a T-only datafile and a joint T+P one. In the case of the high-ℓ part, we also distribute two specific versions of the likelihood, that allow for estimate of T and T+P marginalized over the nuisance parameters. Combining both the low-ℓ and high-ℓ files, one can compute the likelihood over the range ℓ=2-2508 in TT and ℓ=2-1996 in TE and EE. A full description of the low-ℓ and high-ℓ likelihood is available in Planck-2015-A11.

Similarly, for the case of lensing, we also distribute the likelihood using both the reconstruction based on the T map only, or on T+P maps. A full description of the lensing likelihood is available in Planck-2015-A15.

All of the likelihood files have at least one nuisance parameter, allowing users to investigate the Planck absolute calibration. We recommend that his parameter is explored in the Gaussian prior 1.0000±0.0025.

Low-ℓ likelihoods

TT only - commander

This file allows for the computation of the CMB TT likelihood in the range ℓ=2-29. Using the optional tool in the code package, it can be modified to cover any multipole range up to ℓ<200.

Production process

The likelihood is based on the results of the Commander approach, which implements a Bayesian component separation method in pixel space, sampling the posterior distribution of the parameters of a model that describes both the CMB and the foreground emissions in a combination of the Planck maps, the WMAP map, and the 408 MHz survey. The samples of this exploration are used to infer the foreground marginalized low-ℓ likelihood for any TT CMB spectrum.

====== Inputs ======:

• Planck 30 and 44 GHz frequency maps
• Planck 70 to 857 GHz detector and detector-set maps
• 9-year WMAP temperature sky maps between 23 and 94 GHz
• 408 MHz survey of Haslam et al. (1982)
• Commander χ²-based LM93 confidence mask (Planck-2015-A10)

File name and usage

The commander likelihood is distributed in plc_2.0/low_l/commander/commander_rc2_v1.1_l2_29_B.clik

When used with the library, it expects a vector of parameters consisting of the TT CMB power spectrum from ℓ=0 to 29 (inclusive) and an extra nuisance parameter consisting of the overall Planck calibration.

TEB

This file allows for the computation of the CMB joint TT,EE, BB and TE likelihood in the range ℓ=2-29. It should not be used with the low-ℓ TT-only likelihood.

Production process

The file allows for the computation of the pixel based likelihood of T, E, and B maps. The T map is the best-fit map obtained from the commander algorithm, as described above, while the E and B maps are obtained from the 70GHz LFI full mission data, but excluding the second and fourth surveys. Foreground contamination is dealt with by marginalizing over templates based on the 30GHz LFI and 353GHz HFI maps. The covariance matrix comes from the Planck detector sensitivity modulated on the sky by the scanning strategy. The covariance matrix is further enlarged to account for the foreground template removal. To speed up the computation by about an order of magnitude, the problem is projected into C_ℓ space using the Sherman-Morrison-Woodbury identity. Only the projected quantities are stored in the data package.

====== Inputs ======:

• Planck 30 and 44 GHz frequency maps
• Planck 70 to 857 GHz detector and detector-set maps
• 9-year WMAP temperature sky maps between 23 and 94 GHz
• 408 MHz survey of Haslam et al. (1982)
• Commander χ²-based LM93 confidence mask Planck-2015-A10

File name and usage

The low ell TEB likelihood is distributed in plc_2.0/low_l/bflike/lowl_SMW_70_dx11d_2014_10_03_v5c_Ap.clik.

When used with the library, this expects a vector of parameters consisting of the TT CMB power spectrum from ℓ=0 to 29 (inclusive), followed by the EE (ℓ=0 to 29), BB (ℓ=0 to 29) and TE (ℓ=0 to 29) spectra, and by an extra nuisance parameter consisting of the overall Planck calibration.

High-ℓ likelihoods

TT only - plik

This file allows for the computation of the CMB TT likelihood in the range ℓ=30-2508. Using the optional tool in the code package it can be modified to cover any multipole range within 29<ℓ<2509.

Production process

The file contains the 100-GHz, 143-GHz, and 217-GHz binned half-mission TT cross-spectra. Only the 100×100, 143×143, 143×217, and 217×217 are actually used. Masks and multipole ranges for each spectra are different and described in Planck-2015-A11. Masks are based on CMB cleaned 353-GHz map for the dust component, on the Planck catalogs for the point source part, and on the CO maps. The file also contains templates for the residual foreground contamination of each spectra. The templates are needed to allow the computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected by Monte Carlo estimates for the effect of point sources. The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.

====== Inputs ======:

• Planck 100, 143 and 217GHz half mission Temperature maps
• CMB cleaned (using the smica map) 353-GHz map, CO emission maps, and Planck catalogs for the masks.
• Planck 545-GHz maps for the dust residual contamination template
• CIB, tSZ, kSZ, CIBxSZ templates

File name and usage

The high ell, plik TT likelihood is distributed in plc_2.0/hi_l/plik/plik_dx11dr2_HM_v18_TT.clik

When used with the library, it expects a vector of parameters consisting in the TT CMB power spectrum from ℓ=0 to 2508 (inclusive), followed by a vector of 16 nuisance parameters. Those are, in that order

• A_cib_217, The CIB contamination at ℓ=3000 in the 217-GHz Planck map.
• cib_index, The effective slope of the CIB spectrum. This parameter should be set to -1.3
• xi_sz_cib, The szXcib cross-correlation
• A_sz, the tSZ contamination at 143GHz
• ps_A_100_100, the point source contribution in 100×100
• ps_A_143_143, the point source contribution in 143×143
• ps_A_143_217, the point source contribution in 143×217
• ps_A_217_217, the point source contribution in 217×217
• ksz_norm, the ksz contamination
• gal545_A_100, the dust residual contamination at ℓ=200 in 100×100
• gal545_A_143, the dust residual contamination at ℓ=200 in 143×143
• gal545_A_143_217, the dust residual contamination at ℓ=200 in 143×217
• gal545_A_217, the dust residual contamination at ℓ=200 in 217×217
• calib_100T, the relative calibration between the 100 and 143 spectra
• calib_217T, the relative calibration between the 217 and 143 spectra
• A_planck, the Planck absolute calibration

We recommend using the following gaussian priors:

• ksz_norm + 1.6 * A_sz = 9.5 ± 3
• cib_index = -1.3
• gal545_A_100 = 7±2
• gal545_A_143 = 9±2
• gal545_A_143_217 = 21±8.5
• gal545_A_217 = 80±20
• calib_100T = 0.999±0.001
• calib_217T = 0.995±0.002
• A_planck = 1±0.0025

TT+TE+EE - plik

This file allows for the computation of the CMB joint TT, TE and EE likelihood in the range [math]\ell=30-2508[/math] for TT and [math]\ell=30-1996[/math] for TE and EE.

Production process

The file contains the 100-GHz, 143-GHz, and 217-GHz binned half mission T and E cross-spectra. In temperature, only the 100×100, 143×143, 143×217 and 217×217 are actually used, while in TE and EE all of them are. Masks and multipole ranges for each spectra are different and described in Planck-2015-A11. Masks are based on CMB cleaned 353-GHz map for the dust component, on the Planck catalogs for the point source part, and on the CO maps. The file also contains templates for the residual foreground contamination of each spectra. The templates are needed to allow the computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected by Monte-carlo estimates for the effect of point sources. The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.

====== Inputs ======:

• Planck 100, 143, and 217-GHz half-mission T+P maps
• CMB cleaned (using the smica map) 353-GHz map, CO emission maps, and Planck catalogues for the masks.
• Planck 545GHz and 353-GHz maps for the dust residual contamination template
• CIB, tSZ, kSZ, CIBxSZ templates
• best fit CMB+fg model for the beam leakage template

File name and usage

The high ell, plik TTTEEE likelihood is distributed in plc_2.0/hi_l/plik/plik_dx11dr2_HM_v18_TTTEEE.clik

This file should not be used with any other TT only high ell file.

When used with the library, it expects a vector of parameters consisting in the TT CMB power spectrum from [math]\ell=0[/math] to [math]\ell=2508[/math] (included), followed the EE and TE spectra (same range) and by a vector of 94 nuisance parameters. Those are, in that order

• A_cib_217, The CIB contamination at ℓ=3000 in the 217GHz Planck map.
• cib_index, The effective slope of the CIB spectrum. This parameter should be set to -1.3
• xi_sz_cib, The szXcib cross-correlation
• A_sz, the tSZ contamination at 143GHz
• ps_A_100_100, the point source contribution in 100×100
• ps_A_143_143, the point source contribution in 143×143
• ps_A_143_217, the point source contribution in 143×217
• ps_A_217_217, the point source contribution in 217×217
• ksz_norm, the ksz contamination
• gal545_A_100, the dust residual contamination at ℓ=200 in 100×100TT
• gal545_A_143, the dust residual contamination at ℓ=200 in 143×143TT
• gal545_A_143_217, the dust residual contamination at ℓ=200 in 143×217TT
• gal545_A_217, the dust residual contamination at ℓ=200 in 217×217TT
• galf_EE_A_100, The dust residual contamination at ℓ=500 in 100×100EE
• galf_EE_A_100_143, The dust residual contamination at ℓ=500 in 100×143EE
• galf_EE_A_100_217, The dust residual contamination at ℓ=500 in 100×217EE
• galf_EE_A_143, The dust residual contamination at ℓ=500 in 143×143EE
• galf_EE_A_143_217, The dust residual contamination at ℓ=500 in 143×217EE
• galf_EE_A_217, The dust residual contamination at ℓ=500 in 217×217EE
• galf_EE_index, the dust EE template slope, should be set to -2.4
• galf_TE_A_100, The dust residual contamination at ℓ=500 in 100×100TE
• galf_TE_A_100_143, The dust residual contamination at ℓ=500 in 100×143TE
• galf_TE_A_100_217, The dust residual contamination at ℓ=500 in 100×217TE
• galf_TE_A_143, The dust residual contamination at ℓ=500 in 143x143TE
• galf_TE_A_143_217, The dust residual contamination at ℓ=500 in 143×217TE
• galf_TE_A_217, The dust residual contamination at ℓ=500 in 217×217TE
• galf_TE_index, the dust EE template slope, should be set to -2.4
• bleak_epsilon_0_0T_0E, Beam leakage parameters, espilon_0, 100×100 TE
• bleak_epsilon_1_0T_0E, Beam leakage parameters, espilon_1, 100×100 TE
• bleak_epsilon_2_0T_0E, Beam leakage parameters, espilon_2, 100×100 TE
• bleak_epsilon_3_0T_0E, Beam leakage parameters, espilon_3, 100×100 TE
• bleak_epsilon_4_0T_0E, Beam leakage parameters, espilon_4, 100×100 TE
• bleak_epsilon_0_0T_1E, Beam leakage parameters, espilon_0, 100×143 TE
• bleak_epsilon_1_0T_1E, Beam leakage parameters, espilon_1, 100×143 TE
• bleak_epsilon_2_0T_1E, Beam leakage parameters, espilon_2, 100×143 TE
• bleak_epsilon_3_0T_1E, Beam leakage parameters, espilon_3, 100×143 TE
• bleak_epsilon_4_0T_1E, Beam leakage parameters, espilon_4, 100×143 TE
• bleak_epsilon_0_0T_2E, Beam leakage parameters, espilon_0, 100×217 TE
• bleak_epsilon_1_0T_2E, Beam leakage parameters, espilon_1, 100×217 TE
• bleak_epsilon_2_0T_2E, Beam leakage parameters, espilon_2, 100×217 TE
• bleak_epsilon_3_0T_2E, Beam leakage parameters, espilon_3, 100×217 TE
• bleak_epsilon_4_0T_2E, Beam leakage parameters, espilon_4, 100×217 TE
• bleak_epsilon_0_1T_1E, Beam leakage parameters, espilon_0, 143×143 TE
• bleak_epsilon_1_1T_1E, Beam leakage parameters, espilon_1, 143×143 TE
• bleak_epsilon_2_1T_1E, Beam leakage parameters, espilon_2, 143×143 TE
• bleak_epsilon_3_1T_1E, Beam leakage parameters, espilon_3, 143×143 TE
• bleak_epsilon_4_1T_1E, Beam leakage parameters, espilon_4, 143×143 TE
• bleak_epsilon_0_1T_2E, Beam leakage parameters, espilon_0, 143×217 TE
• bleak_epsilon_1_1T_2E, Beam leakage parameters, espilon_1, 143×217 TE
• bleak_epsilon_2_1T_2E, Beam leakage parameters, espilon_2, 143×217 TE
• bleak_epsilon_3_1T_2E, Beam leakage parameters, espilon_3, 143×217 TE
• bleak_epsilon_4_1T_2E, Beam leakage parameters, espilon_4, 143×217 TE
• bleak_epsilon_0_2T_2E, Beam leakage parameters, espilon_0, 217×217 TE
• bleak_epsilon_1_2T_2E, Beam leakage parameters, espilon_1, 217×217 TE
• bleak_epsilon_2_2T_2E, Beam leakage parameters, espilon_2, 217×217 TE
• bleak_epsilon_3_2T_2E, Beam leakage parameters, espilon_3, 217×217 TE
• bleak_epsilon_4_2T_2E, Beam leakage parameters, espilon_4, 217×217 TE
• bleak_epsilon_0_0E_0E, Beam leakage parameters, espilon_0, 100×100 EE
• bleak_epsilon_1_0E_0E, Beam leakage parameters, espilon_1, 100×100 EE
• bleak_epsilon_2_0E_0E, Beam leakage parameters, espilon_2, 100×100 EE
• bleak_epsilon_3_0E_0E, Beam leakage parameters, espilon_3, 100×100 EE
• bleak_epsilon_4_0E_0E, Beam leakage parameters, espilon_4, 100×100 EE
• bleak_epsilon_0_0E_1E, Beam leakage parameters, espilon_0, 100×143 EE
• bleak_epsilon_1_0E_1E, Beam leakage parameters, espilon_1, 100×143 EE
• bleak_epsilon_2_0E_1E, Beam leakage parameters, espilon_2, 100×143 EE
• bleak_epsilon_3_0E_1E, Beam leakage parameters, espilon_3, 100×143 EE
• bleak_epsilon_4_0E_1E, Beam leakage parameters, espilon_4, 100×143 EE
• bleak_epsilon_0_0E_2E, Beam leakage parameters, espilon_0, 100×217 EE
• bleak_epsilon_1_0E_2E, Beam leakage parameters, espilon_1, 100×217 EE
• bleak_epsilon_2_0E_2E, Beam leakage parameters, espilon_2, 100×217 EE
• bleak_epsilon_3_0E_2E, Beam leakage parameters, espilon_3, 100×217 EE
• bleak_epsilon_4_0E_2E, Beam leakage parameters, espilon_4, 100×217 EE
• bleak_epsilon_0_1E_1E, Beam leakage parameters, espilon_0, 143×143 EE
• bleak_epsilon_1_1E_1E, Beam leakage parameters, espilon_1, 143×143 EE
• bleak_epsilon_2_1E_1E, Beam leakage parameters, espilon_2, 143×143 EE
• bleak_epsilon_3_1E_1E, Beam leakage parameters, espilon_3, 143×143 EE
• bleak_epsilon_4_1E_1E, Beam leakage parameters, espilon_4, 143×143 EE
• bleak_epsilon_0_1E_2E, Beam leakage parameters, espilon_0, 143×217 EE
• bleak_epsilon_1_1E_2E, Beam leakage parameters, espilon_1, 143×217 EE
• bleak_epsilon_2_1E_2E, Beam leakage parameters, espilon_2, 143×217 EE
• bleak_epsilon_3_1E_2E, Beam leakage parameters, espilon_3, 143×217 EE
• bleak_epsilon_4_1E_2E, Beam leakage parameters, espilon_4, 143×217 EE
• bleak_epsilon_0_2E_2E, Beam leakage parameters, espilon_0, 217×217 EE
• bleak_epsilon_1_2E_2E, Beam leakage parameters, espilon_1, 217×217 EE
• bleak_epsilon_2_2E_2E, Beam leakage parameters, espilon_2, 217×217 EE
• bleak_epsilon_3_2E_2E, Beam leakage parameters, espilon_3, 217×217 EE
• bleak_epsilon_4_2E_2E, Beam leakage parameters, espilon_4, 217×217 EE
• calib_100T, the relative calibration between the 100 and 143 TT spectra
• calib_217T, the relative calibration between the 217 and 143 TT spectra
• calib_100P, the calibration of the 100 EE spectra, This should be set to 1
• calib_143P, the calibration of the 143 EE spectra, This should be set to 1
• calib_217P, the calibration of the 217 EE spectra, This should be set to 1
• A_pol, the calibration of the polarization relative to the temperature, this should be set to 1
• A_planck, the Planck absolute calibration

We recommend using the following gaussian priors:

• ksz_norm + 1.6 * A_sz = 9.5 ± 3)
• cib_index = -1.3
• gal545_A_100 = 7±2
• gal545_A_143 = 9±2
• gal545_A_143_217 = 21±8.5
• gal545_A_217 = 80±20
• galf_EE_A_100 = 0.06±0.012
• galf_EE_A_100_143 = 0.05±0.015
• galf_EE_A_100_217 = 0.11±0.033
• galf_EE_A_143 = 0.1±0.02
• galf_EE_A_143_217 = 0.24±0.048
• galf_EE_A_217 = 0.72±0.14
• galf_EE_index = -2.4
• galf_TE_A_100 = 0.14±0.042
• galf_TE_A_100_143 = 0.12±0.036
• galf_TE_A_100_217 = 0.3±0.09
• galf_TE_A_143 = 0.24±0.072
• galf_TE_A_143_217 = 0.6±0.018
• galf_TE_A_217 = 1.8±0.54
• galf_TE_index = -2.4
• bleak_epsilon_0_0T_0E = 0
• bleak_epsilon_1_0T_0E = 0
• bleak_epsilon_2_0T_0E = 0
• bleak_epsilon_3_0T_0E = 0
• bleak_epsilon_4_0T_0E = 0
• bleak_epsilon_0_0T_1E = 0
• bleak_epsilon_1_0T_1E = 0
• bleak_epsilon_2_0T_1E = 0
• bleak_epsilon_3_0T_1E = 0
• bleak_epsilon_4_0T_1E = 0
• bleak_epsilon_0_0T_2E = 0
• bleak_epsilon_1_0T_2E = 0
• bleak_epsilon_2_0T_2E = 0
• bleak_epsilon_3_0T_2E = 0
• bleak_epsilon_4_0T_2E = 0
• bleak_epsilon_0_1T_1E = 0
• bleak_epsilon_1_1T_1E = 0
• bleak_epsilon_2_1T_1E = 0
• bleak_epsilon_3_1T_1E = 0
• bleak_epsilon_4_1T_1E = 0
• bleak_epsilon_0_1T_2E = 0
• bleak_epsilon_1_1T_2E = 0
• bleak_epsilon_2_1T_2E = 0
• bleak_epsilon_3_1T_2E = 0
• bleak_epsilon_4_1T_2E = 0
• bleak_epsilon_0_2T_2E = 0
• bleak_epsilon_1_2T_2E = 0
• bleak_epsilon_2_2T_2E = 0
• bleak_epsilon_3_2T_2E = 0
• bleak_epsilon_4_2T_2E = 0
• bleak_epsilon_0_0E_0E = 0
• bleak_epsilon_1_0E_0E = 0
• bleak_epsilon_2_0E_0E = 0
• bleak_epsilon_3_0E_0E = 0
• bleak_epsilon_4_0E_0E = 0
• bleak_epsilon_0_0E_1E = 0
• bleak_epsilon_1_0E_1E = 0
• bleak_epsilon_2_0E_1E = 0
• bleak_epsilon_3_0E_1E = 0
• bleak_epsilon_4_0E_1E = 0
• bleak_epsilon_0_0E_2E = 0
• bleak_epsilon_1_0E_2E = 0
• bleak_epsilon_2_0E_2E = 0
• bleak_epsilon_3_0E_2E = 0
• bleak_epsilon_4_0E_2E = 0
• bleak_epsilon_0_1E_1E = 0
• bleak_epsilon_1_1E_1E = 0
• bleak_epsilon_2_1E_1E = 0
• bleak_epsilon_3_1E_1E = 0
• bleak_epsilon_4_1E_1E = 0
• bleak_epsilon_0_1E_2E = 0
• bleak_epsilon_1_1E_2E = 0
• bleak_epsilon_2_1E_2E = 0
• bleak_epsilon_3_1E_2E = 0
• bleak_epsilon_4_1E_2E = 0
• bleak_epsilon_0_2E_2E = 0
• bleak_epsilon_1_2E_2E = 0
• bleak_epsilon_2_2E_2E = 0
• bleak_epsilon_3_2E_2E = 0
• bleak_epsilon_4_2E_2E = 0
• calib_100T = 0.999±0.001
• calib_217T = 0.995±0.002
• calib_100P = 1
• calib_143P = 1
• calib_217P = 1
• A_pol = 1
• A_planck = 1±0.0025

TT only - plik lite

This file allows for the computation of the nuisance marginalized CMB TT likelihood in the range [math]\ell=30-2508[/math]. It should not be used with the regular high ell TT or TTTEEE files.

Production process

The plik likelihood file described above have been explored using a Bayesian algorithm described in Planck-2015-A11. The joint posterior of the CMB TT spectrum, marginalized over the nuisance parameters has been extracted from this exploration to build a high ell likelihood approximation for the CMB spectrum only. The nuisance parameters have been marginalized in the priors described above.

====== Inputs ======:

• plik plik_dx11dr2_HM_v18_TT likelihood
• dust residual, CIB, tSZ, kSZ, CIBxSZ templates

File name and usage

The high ell, plik TT, nuisance marginalized likelihood is distributed in plc_2.0/hi_l/plik_lite/plik_lite_v18_TT.clik

When used with the library, it expects a vector of parameters consisting in the TT CMB power spectrum from ℓell=0 to 2508 (inclusive), followed by the Planck absolute calibration nuisance parameter.

TT EE TE - plik lite

This file allows for the computation of the nuisance marginalized CMB joint TT, TE, EE likelihood in the range ℓ=30-2508 for temperature and ℓ=30-1996 for TE and EE. It should not be used with the regular high-ℓ TT or TTTEEE files.

Production process

The plik likelihood file described above have been explored using a Bayesian algorithm described in Planck-2015-A11. The joint posterior of the CMB TT, TE, and EE spectra, marginalized over the nuisance parameters has been extracted from this exploration to build a high ell likelihood approximation for the CMB spectrum only. The nuisance parameters have been marginalized in the priors described above.

====== Inputs ======:

• plik plik_dx11dr2_HM_v18_TTTEEE likelihood
• dust residual, CIB, tSZ, kSZ, CIBxSZ templates

File name and usage

The high ell, plik TTTEEE, nuisance marginalized likelihood is distributed in plc_2.0/hi_l/plik_lite/plik_lite_v18_TTTEEE.clik

When used with the library, it expects a vector of parameters consisting in the TT CMB power spectrum from [math]\ell=0[/math] to [math]\ell=2508[/math] (included), followed by the EE, and TE spectra (same range) and by the Planck absolute calibration nuisance parameter.

lensing likelihoods

T only

This file allows for the computation of baseline lensing likelihood, using only the Temperature smica map based lensing reconstruction. Only the lensing multipole range [math]\ell=40-400[/math]. is used. Covariance matrix for the likelihood are based on Monte Carlo. The likelihood includes the computation of model dependent correction to the normalization and the N1 bias. To speed up those computation, both are performed using a linear approximation.

Production process

The smica temperature maps are filtered and correlated to reconstruct an optimal lensing reconstruction map. Biases are corrected using Monte-Carlo simulations for the dominant mean field and N0 contribution, while the N1 bias is computed using the CMB based best fit model. The power spectrum of this map is measured out of a large mask, and binned. The covariance matrix of this binned spectrum is evaluated using numerical simulations. The model dependent corrections to the normalization and N1 biases are linearized and precomputed.

====== Inputs ======:

• smica T CMB map
• 60% galactic mask
• best fit Planck CMB model

File name and usage

The T only lensing likelihood is distributed in plc_2.0/hi_l/lensing/smica_g30_ftl_full_pttptt.clik_lensing

When used with the library, it expects a vector of parameters consisting in the phiphi lensing spectrum ([math]\ell=0[/math] to [math]\ell=2048[/math] included), followed by the TT CMB power spectrum ([math]\ell=0[/math] to [math]\ell=2048[/math] included) and by the Planck absolute calibration nuisance parameter.

The TT spectrum is needed to compute the normalization and N1 corrections.

T+P

This file allows for the computation of baseline lensing likelihood, using both the Temperature and polarization smica map based lensing reconstruction. Only the lensing multipole range[math]\ell=40-400[/math]. is used. Covariance matrix for the likelihood are based on Monte Carlo. The likelihood includes the computation of model dependent correction to the normalization and the N1 bias. To speed up those computation, both are performed using a linear approximation.

Production process

The smica temperature and polarization maps are filtered and correlated to reconstruct an optimal lensing reconstruction map. Biases are corrected using Monte-Carlo simulations for the dominant mean field and N0 contribution, while the N1 bias is computed using the CMB based best fit model. The power spectrum of this map is measured out of a large mask, and binned. The covariance matrix of this binned spectrum is evaluated using numerical simulations. The model dependent corrections to the normalization and N1 biases are linearized and precomputed.

====== Inputs ======:

• SMICA T and E CMB map;
• 60% Galactic mask;
• best fit Planck CMB model.

File name and usage

The T+P only lensing likelihood is distributed in plc_2.0/hi_l/lensing/smica_g30_ftl_full_pp.clik_lensing.

When used with the library, this expects a vector of parameters consisting of the φφ lensing spectrum (ℓ=0 to 2048 inclusive), followed by the TT CMB power spectrum (ℓ=0 to 2048 inclusive), the EE and TE power spectra(same range) and by the Planck absolute calibration nuisance parameter.

The TT, EE, and TE spectra are needed to compute the normalization and N1 corrections.

Data sets - Extended data

Four other data files are available. Those extend the baseline delivery by adding

• TE, EE and other joint high-[math]\ell[/math] plik likelihoods. COM_Likelihood_Data-extra-plik-HM-ext.R2.00.tar.gz
• TT and TTTEEE unbinned high-[math]\ell[/math] plik likelihood. COM_Likelihood_Data-extra-plik-unbinned.R2.00.tar.gz
• TT, TE, EE and TTTEEE plik likelihood using the detset cross-spectra instead of the half mission one, and including empirical correlated noise correction for the TT spectra. COM_Likelihood_Data-extra-plik-DS.R2.00.tar.gz
• extended [math]\ell[/math] range lensing likelihood. COM_Likelihood_Data-extra-lensing-ext.R2.00.tar.gz

Note that if this products are discussed in the Planck papers, they are not used for the baseline results.

Each file contains a readme.md description of the content and the use of the different likelihood files.

References