Difference between revisions of "Map-making LFI"

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==Noise Monte Carlo Simulation==
 
==Noise Monte Carlo Simulation==
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===Overview===
 
Calculating and handling full pixel-to-pixel noise covariances in Planck maps if feasible only at low resolution.
 
Calculating and handling full pixel-to-pixel noise covariances in Planck maps if feasible only at low resolution.
 
To support the analysis of high-resolution maps, a Monte Carlo set of noise maps were produced.
 
To support the analysis of high-resolution maps, a Monte Carlo set of noise maps were produced.
 
These noise Monte Carlos were produced at two levels of the analysis:  1) LFI Monte Carlo (MC) as part of the LFI data processing, and 2) Full Focal Plane (FFP) Monte Carlo as part of the joint HFI/LFI data processing. This page describes the LFI noise MC.  For the FFP MC, see [[HL-sims]].
 
These noise Monte Carlos were produced at two levels of the analysis:  1) LFI Monte Carlo (MC) as part of the LFI data processing, and 2) Full Focal Plane (FFP) Monte Carlo as part of the joint HFI/LFI data processing. This page describes the LFI noise MC.  For the FFP MC, see [[HL-sims]].
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===Inputs===
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The noise MC assumes a three-parameter noise model: white noise level ($\sigma$), slope, and knee frequency ($f_\mathrm{knee}$), so that the noise power spectrum is assumed to have the form
 +
$ P(f) = \frac{2\sigma^2}{f_\mathrm{sample}}\left(\frac{f}{f_\mathrm{knee}}\right)^\mathrm{slope} $.  Here $f_\mathrm{sample}$ is the sampling frequency of the instrument. The noise parameters were determined separately for each radiometer

Revision as of 10:09, 19 October 2012

Noise Monte Carlo Simulation[edit]

Overview[edit]

Calculating and handling full pixel-to-pixel noise covariances in Planck maps if feasible only at low resolution. To support the analysis of high-resolution maps, a Monte Carlo set of noise maps were produced. These noise Monte Carlos were produced at two levels of the analysis: 1) LFI Monte Carlo (MC) as part of the LFI data processing, and 2) Full Focal Plane (FFP) Monte Carlo as part of the joint HFI/LFI data processing. This page describes the LFI noise MC. For the FFP MC, see HL-sims.

Inputs[edit]

The noise MC assumes a three-parameter noise model: white noise level ($\sigma$), slope, and knee frequency ($f_\mathrm{knee}$), so that the noise power spectrum is assumed to have the form $ P(f) = \frac{2\sigma^2}{f_\mathrm{sample}}\left(\frac{f}{f_\mathrm{knee}}\right)^\mathrm{slope} $. Here $f_\mathrm{sample}$ is the sampling frequency of the instrument. The noise parameters were determined separately for each radiometer

(Planck) Low Frequency Instrument

(Planck) High Frequency Instrument