Bayesian component separation and CMB estimation for the 5-year WMAP temperature data

TL;DR

This paper applies a validated Gibbs sampling code to WMAP 5-year data for component separation and CMB power spectrum estimation, confirming previous results and robustness against foreground modeling assumptions.

Contribution

It introduces a comprehensive Gibbs sampling approach for component separation in WMAP data, validating results and testing foreground assumptions.

Findings

Low-$ll$ CMB power spectrum matches published results.

Residual monopoles and dipoles are negligible.

Power asymmetry between hemispheres is robust.

Abstract

A well-tested and validated Gibbs sampling code, that performs component separation and CMB power spectrum estimation, was applied to the {\it WMAP} 5-yr data. Using a simple model consisting of CMB, noise, monopoles and dipoles, a ``per pixel'' low-frequency power-law (fitting for both amplitude and spectral index), and a thermal dust template with fixed spectral index, we found that the low-$\ell$ ($\ell < 50$) CMB power spectrum is in good agreement with the published {\it WMAP}5 results. Residual monopoles and dipoles were found to be small ($\lesssim 3 \mu$K) or negligible in the 5-yr data. We comprehensively tested the assumptions that were made about the foregrounds (e.g. dust spectral index, power-law spectral index prior, templates), and found that the CMB power spectrum was insensitive to these choices. We confirm the asymmetry of power between the north and south ecliptic hemispheres, which appears to be robust against foreground modeling. The map of low frequency spectral indices indicates a steeper spectrum on average ($\beta=-2.97\pm0.21$) relative to those found at low ($\sim$GHz) frequencies.