WMAP 9-year CMB estimation using sparsity

TL;DR

This paper introduces a new method for extracting the CMB from WMAP data using sparsity in the wavelet domain, resulting in a high-resolution map consistent with previous estimates.

Contribution

The paper presents a novel sparse component separation technique, LGMCA, applied to WMAP data, improving CMB map reconstruction with a different criterion than traditional methods.

Findings

LGMCA produces a 15 arcmin resolution CMB map from WMAP 9-year data.

The reconstructed map's power spectrum aligns with existing estimates.

The sparsity-based approach offers a new perspective on component separation.

Abstract

Recovering the Cosmic Microwave Background (CMB) from WMAP data requires galactic foreground emissions to be accurately separated out. Most component separation techniques rely on second order statistics such as Internal Linear Combination (ILC) techniques. In this paper, we present a new WMAP 9-year CMB map, with 15 arcmin resolution, which is reconstructed using a recently introduced sparse component separation technique, coined Local Generalized Morphological Component Analysis (LGMCA). LGMCA emphasizes on the sparsity of the components to be retrieved in the wavelet domain. We show that although derived from a radically different separation criterion ({i.e. sparsity), the LGMCA-WMAP 9 map and its power spectrum are fully consistent with their more recent estimates from WMAP 9.