CMB data analysis and sparsity

TL;DR

This paper presents a novel inpainting algorithm for CMB data on the sphere, using sparse representations to effectively handle masked regions and improve statistical analysis for cosmological parameter estimation.

Contribution

It introduces a new sphere-based inpainting method leveraging sparsity, addressing data gaps caused by astrophysical contamination in CMB observations.

Findings

Improved handling of masked regions in CMB maps.

Enhanced statistical analysis accuracy for cosmological parameters.

Potential for better separation of astrophysical signals.

Abstract

The statistical analysis of the soon to come Planck satellite CMB data will help set tighter bounds on major cosmological parameters. On the way, a number of practical difficulties need to be tackled, notably that several other astrophysical sources emit radiation in the frequency range of CMB observations. Some level of residual contributions, most significantly in the galactic region and at the locations of strong radio point sources will unavoidably contaminate the estimated spherical CMB map. Masking out these regions is common practice but the gaps in the data need proper handling. In order to restore the stationarity of a partly incomplete CMB map and thus lower the impact of the gaps on non-local statistical tests, we developed an inpainting algorithm on the sphere based on a sparse representation of the data, to fill in and interpolate across the masked regions.