Improved Gibbs samplers for Cosmic Microwave Background power spectrum estimation

TL;DR

This paper evaluates various Gibbs sampler variants for estimating the CMB power spectrum, demonstrating that a new approach significantly improves efficiency over traditional methods in nearly full-sky scenarios.

Contribution

The paper introduces and assesses new Gibbs sampler variants, especially the Centered overrelax approach, for more efficient CMB power spectrum estimation.

Findings

Centered overrelax outperforms standard Gibbs by up to 100x in efficiency.

The new method is effective on full and cut sky simulations.

It offers a promising alternative for CMB data analysis.

Abstract

We study different variants of the Gibbs sampler algorithm from the perspective of their applicability to the estimation of power spectra of the cosmic microwave background (CMB) anisotropies. These include approaches studied earlier in the CMB literature as well as new ones which are proposed in this work. We demonstrate all these variants on full and cut sky simulations and compare their performance, assessing both their computational and statistical efficiency. For this we employ a consistent comparison metric, an effective sample size (ESS) per second, commonly used in this context in the statistical literature. We show that one of the proposed approaches, referred to as Centered overrelax, which capitalizes on additional, auxiliary variables to minimize computational time needed per sample, and uses overrelaxation to decorrelate subsequent samples, performs better than the standard Gibbs sampler by a factor between one and two orders of magnitude in the nearly full-sky, satellite-like cases. It therefore potentially provides an interesting alternative to the currently favored approaches.