Difference between revisions of "NoiseCovarMatrices"
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| + | == Introduction == | ||
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| + | The Planck low resolution frequency maps are provided with a pixel-pixel noise covariance matrix. The matrix is required for maximum likelihood analysis of CMB anisotropies in the maps but also for low resolution component separation. | ||
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| + | The first step in calculating the noise matrices is estimating the detector noise. We do this for each detector at roughly one day intervals. The process is | ||
| + | # time-ordered information (TOI) is cleaned of signal by interpolating the full frequency, full mission map to the sample positions on the sky. | ||
| + | # Estimate the auto covariance function of the TOI without samples that either have their quality flag raised or that fall within the galactic or point source mask | ||
| + | # Fourier transform the auto covariance function into a power spectral density (PSD) | ||
| + | # Reduce realization noise in the PSD by fitting and analytical model | ||
Revision as of 22:03, 18 December 2014
Introduction[edit]
The Planck low resolution frequency maps are provided with a pixel-pixel noise covariance matrix. The matrix is required for maximum likelihood analysis of CMB anisotropies in the maps but also for low resolution component separation.
The first step in calculating the noise matrices is estimating the detector noise. We do this for each detector at roughly one day intervals. The process is
- time-ordered information (TOI) is cleaned of signal by interpolating the full frequency, full mission map to the sample positions on the sky.
- Estimate the auto covariance function of the TOI without samples that either have their quality flag raised or that fall within the galactic or point source mask
- Fourier transform the auto covariance function into a power spectral density (PSD)
- Reduce realization noise in the PSD by fitting and analytical model
Cosmic Microwave background
