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 ($\lesssim 3 \mu$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.
