Cosmic Microwave Background Likelihood Approximation by a Gaussianized Blackwell-Rao Estimator

We introduce a new cosmic microwave background (CMB) temperature likelihood approximation called the Gaussianized Blackwell-Rao estimator. This estimator is derived by transforming the observed marginal power spectrum distributions obtained by the CMB Gibbs sampler into standard univariate Gaussians, and then approximating their joint transformed distribution by a multivariate Gaussian. The method is exact for full-sky coverage and uniform noise and an excellent approximation for sky cuts and scanning patterns relevant for modern satellite experiments such as the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. The result is a stable, accurate, and computationally very efficient CMB temperature likelihood representation that allows the user to exploit the unique error propagation capabilities of the Gibbs sampler to high s. A single evaluation of this estimator between = 2 and 200 takes ~0.2 CPU milliseconds, while for comparison, a singe pixel space likelihood evaluation between = 2 and 30 for a map with ~2500 pixels requires ~20 s. We apply this tool to the five-year WMAP temperature data, and re-estimate the angular temperature power spectrum, C , and likelihood, , for