Background-Source Separation in astronomical images with Bayesian probability theory (I): the method

TL;DR

This paper introduces a Bayesian probabilistic method for separating background and sources in astronomical images, enabling detection and characterization of faint, extended celestial objects with quantified uncertainties.

Contribution

It presents a novel Bayesian mixture model combined with multi-resolution analysis for automatic detection and parameter estimation of celestial sources.

Findings

Effective separation of background and sources in astronomical images.

Automatic parameterization of detected sources including position and morphology.

Quantification of uncertainties in source and background estimates.

Abstract

A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multi-resolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parameterized automatically providing source position, net counts, morphological parameters and their errors.