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

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 ($\lesssim 3 μ$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 ($β=-2.97\pm0.21$) relative to those found at low ($\sim$GHz) frequencies.