A Foreground Cleaned CMB Map from Non-Gaussianity Measurement

TL;DR

This paper introduces a novel method to produce a foreground-cleaned CMB map by minimizing non-Gaussian features using kurtosis, validated through simulations, and matching WMAP results without bias at low multipoles.

Contribution

The paper presents a new foreground removal technique based on kurtosis minimization that avoids bias in the low multipole power spectrum, improving CMB map cleaning.

Findings

The method effectively reduces foreground contamination in the CMB map.

The cleaned map aligns well with WMAP's ILC map outside the galactic plane.

The derived power spectrum matches previous WMAP results without low-multipole bias.

Abstract

In this paper we present a new method to estimate a foreground cleaned Cosmic Microwave Background (CMB) map at a resolution of $1^\circ$ by minimizing the non-Gaussian properties of the cleaned map which arise dominantly due to diffuse foreground emission components from the Milky Way. We employ simple kurtosis statistic as the measure of non-Gaussian properties and perform a linear combination of 5 frequency maps provided by Wilkinson Microwave Anisotropy Probe (WMAP) in its 7 year data release in such a way that the cleaned map has a minimum kurtosis which leads to a non-Gaussianity minimized, foreground cleaned CMB map. We validate the method by performing Monte-Carlo simulations. To minimize any residual foreground contamination from the cleaned map we flag out region near the galactic plane based upon results from simulations. Outside the masked region our new estimate of CMB map matches well with the WMAP's ILC map. A simple pseudo-$C_l$ based CMB TT power spectrum derived from the non-gaussianity minimized map reproduces the earlier results of WMAP's power spectrum. {\it An important advantage of the method is that it does not introduce any negative bias in angular power spectrum in low multipole regime, unlike usual ILC method.} Comparing our results with the previously published results we argue that CMB results are robust with respect to specific foreground removal algorithms employed.