Bayesian component separation: The Planck experience

TL;DR

Bayesian component separation, exemplified by Planck, offers a comprehensive, physically motivated approach to data analysis that effectively constrains parameters and identifies residual systematics, with increasing relevance for future intensity mapping experiments.

Contribution

This paper reviews the application of Bayesian component separation in Planck, highlighting its advantages, challenges, and potential growth in importance for future cosmological experiments.

Findings

Bayesian methods enable robust residual analysis in Planck data.

Physical modeling constrains diverse cosmological and instrumental parameters.

Computational costs are significant but justified by improved data fidelity.

Abstract

Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.