Forecasting the performance of the Minimally Informed foreground cleaning method for CMB polarization observations

TL;DR

This paper develops and validates a semi-analytical forecasting framework for a minimally informed foreground cleaning method in CMB polarization studies, demonstrating its robustness and effectiveness in bias correction using current observational data.

Contribution

It introduces a semi-analytical performance forecasting tool for the minimally informed foreground cleaning method and validates its accuracy against existing sampling techniques.

Findings

The forecasting framework accurately predicts method performance.

Bias correction is effectively regularized by current observational data.

The minimally informed method is robust in foreground subtraction.

Abstract

Astrophysical foreground substraction is crucial to retrieve the cosmic microwave background (CMB) polarization out of the observed data. Recent efforts have been carried out towards the development of a minimally informed component separation method to handle a priori unknown foreground spectral energy distributions (SEDs), while being able to estimate both cosmological, foreground, and potentially instrumental parameters, jointly. In this paper, we develop a semi-analytical performance forecasting framework for the minimally informed method and we validate it by comparing its results against direct sampling of the harmonic-based likelihood and the pixel domain implementation MICMAC. We then use the forecasting tool to demonstrate the robustness of the bias correction procedure introduced in the minimally informed approach. We find that a data-driven approach based on the currently available observational data is enough to efficiently regularize the bias of the method.