Bayesian mixture models for Poisson astronomical images

TL;DR

This paper introduces a Bayesian mixture model technique for detecting and characterizing faint, extended sources in Poisson-regime astronomical images, effectively estimating backgrounds and sources simultaneously.

Contribution

It presents a novel Bayesian mixture model approach for background and source separation in Poisson astronomical images, capable of handling diverse morphologies and uncertainties.

Findings

Successfully applied to ROSAT and Chandra data sets

Revealed a large variety of source morphologies

Feasibility demonstrated for eROSITA data analysis

Abstract

Astronomical images in the Poisson regime are typically characterized by a spatially varying cosmic background, large variety of source morphologies and intensities, data incompleteness, steep gradients in the data, and few photon counts per pixel. The Background-Source separation technique is developed with the aim to detect faint and extended sources in astronomical images characterized by Poisson statistics. The technique employs Bayesian mixture models to reliably detect the background as well as the sources with their respective uncertainties. Background estimation and source detection is achieved in a single algorithm. A large variety of source morphologies is revealed. The technique is applied in the X-ray part of the electromagnetic spectrum on ROSAT and Chandra data sets and it is under a feasibility study for the forthcoming eROSITA mission.