Bayesian Inference of Polarized CMB Power Spectra from Interferometric Data

TL;DR

This paper introduces a Bayesian Gibbs sampling method for analyzing interferometric CMB polarization data to infer B-mode power spectra, addressing detection challenges with a computationally efficient approach.

Contribution

It presents a novel Bayesian inference technique using Gibbs sampling for CMB polarization data analysis from interferometers, improving efficiency and handling realistic observational effects.

Findings

Validates the method with simulated data under realistic conditions

Demonstrates computational efficiency of O(n^{3/2})

Shows potential for analyzing upcoming cosmology observations

Abstract

Detection of B-mode polarization of the cosmic microwave background (CMB) radiation is one of the frontiers of observational cosmology. Because they are an order of magnitude fainter than E-modes, it is quite a challenge to detect B-modes. Having more manageable systematics, interferometers prove to have a substantial advantage over imagers in detecting such faint signals. Here, we present a method for Bayesian inference of power spectra and signal reconstruction from interferometric data of the CMB polarization signal by using the technique of Gibbs sampling. We demonstrate the validity of the method in the flat-sky approximation for a simulation of an interferometric observation on a finite patch with incomplete uv-plane coverage, a finite beam size and a realistic noise model. With a computational complexity of O(n^{3/2}), n being the data size, Gibbs sampling provides an efficient method for analyzing upcoming cosmology observations.