A Bayesian technique for the detection of point sources in CMB maps

TL;DR

This paper introduces a Bayesian MAP detection method for identifying point sources in CMB maps, improving detection completeness and allowing non-arbitrary source count fixing in simulated Planck data.

Contribution

A novel Bayesian MAP approach that incorporates prior information for more effective point source detection in CMB maps, outperforming traditional methods.

Findings

More complete source catalogues at similar spurious source levels.

Ability to fix the number of detected sources non-arbitrarily.

Effective application to simulated Planck satellite data.

Abstract

The detection and flux estimation of point sources in cosmic microwave background (CMB) maps is a very important task in order to clean the maps and also to obtain relevant astrophysical information. In this paper we propose a maximum a posteriori (MAP) approach detection method in a Bayesian scheme which incorporates prior information about the source flux distribution, the locations and the number of sources. We apply this method to CMB simulations with the characteristics of the Planck satellite channels at 30, 44, 70 and 100 GHz. With a similar level of spurious sources, our method yields more complete catalogues than the matched filter with a 5 sigma threshold. Besides, the new technique allows us to fix the number of detected sources in a non-arbitrary way.