INDEPENDENT COMPONENT SEPARATION FROM INCOMPLETE SPHERICAL DATA USING WAVELETS. APPLICATION TO CMB DATA ANALYSIS.
Y. Moudden, P. Abrial, Patricio Vielva, J.-B. Melin, Jean‐Luc Starck, J.-F. Cardoso, Jacques Delabrouille, Maï K. Nguyen · 2005
Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space that was designed to address in a flexible way some of the gen-eral problems raised by Cosmic Microwave Background data analysis. However, a common issue in astronomical data anal-ysis is that the observations are unevenly sampled or incom-plete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are not well mod-eled as stationary processes over the sky. These effects impair data processing techniques in the spherical harmonics repre-sentation. This paper describes a new wavelet transform for spherical maps and proposes an extension of SMICA in this space-scale representation. 1.