We develop a new Bayesian method for estimating white noise levels in CMB sky maps , and apply this algorithm to the 5-year WMAP data .
We assume that the amplitude of the noise RMS is scaled by a constant value , \alpha , relative to a pre-specified noise level .
We then derive the corresponding conditional density , P ( \alpha | s,C _ { \ell } ,d ) , which is subsequently integrated into a general CMB Gibbs sampler .
We first verify our code by analyzing simulated data sets , and then apply the framework to the WMAP data .
For the foreground-reduced 5-year WMAP sky maps and the nominal noise levels initially provided in the 5-year data release , we find that the posterior means typically range between \alpha = 1.005 \pm 0.001 and \alpha = 1.010 \pm 0.001 depending on differencing assembly , indicating that the noise level of these maps are biased low by 0.5-1.0 % .
The same problem is not observed for the uncorrected WMAP sky maps .
After the preprint version of this letter appeared on astro-ph. , the WMAP team has corrected the values presented on their web page , noting that the initially provided values were in fact estimates from the 3-year data release , not from the 5-year estimates .
However , internally in their 5-year analysis the correct noise values were used , and no cosmological results are therefore compromised by this error .
Thus , our method has already been demonstrated in practice to be both useful and accurate .