Semi-blind Bayesian inference of CMB map and power spectrum

TL;DR

This paper introduces a new Bayesian method for analyzing CMB data that is fully blind to physical models, unifying component separation and power spectrum inference while allowing external priors.

Contribution

It presents a novel phenomenological, semi-blind Bayesian framework that integrates multiple analysis steps for CMB data without relying on detailed physical models.

Findings

Efficient sampling scheme exploring component separation uncertainties.

Flexible incorporation of external priors for component separation.

Unification of analysis steps for high-resolution all-sky CMB data.

Abstract

We present a new blind formulation of the Cosmic Microwave Background (CMB) inference problem. The approach relies on a phenomenological model of the multi-frequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that have previously been treated separately, such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, SMICA and ILC, and discuss possible future extensions.