Bayesian CMB foreground separation with a correlated log-normal model

TL;DR

This paper introduces a Bayesian method for separating CMB and foreground signals using a correlated log-normal model, improving over traditional Gaussian priors by capturing complex spatial correlations.

Contribution

It proposes a novel log-normal prior model for foreground components, incorporating spatial and cross-component correlations, enhancing CMB foreground separation accuracy.

Findings

Demonstrates superior performance over flat priors in case studies

Shows improved accuracy in low-resolution foreground separation

Validates the model's effectiveness with real data simulations

Abstract

The extraction of foreground and CMB maps from multi-frequency observations relies mostly on the different frequency behavior of the different components. Existing Bayesian methods additionally make use of a Gaussian prior for the CMB whose correlation structure is described by an unknown angular power spectrum. We argue for the natural extension of this by using non-trivial priors also for the foreground components. Focusing on diffuse Galactic foregrounds, we propose a log-normal model including unknown spatial correlations within each component and cross-correlations between the different foreground components. We present case studies at low resolution that demonstrate the superior performance of this model when compared to an analysis with flat priors for all components.