Disentangling Overlapping Astronomical Sources using Spatial and Spectral Information

TL;DR

This paper introduces a Bayesian algorithm that effectively separates overlapping astronomical sources by integrating spatial and spectral data, improving source identification and parameter estimation in X-ray observations.

Contribution

The novel Bayesian method combines spatial and spectral information to jointly infer the number of sources, assign photons, and estimate source parameters, with quantified uncertainties.

Findings

Demonstrated improved source separation in simulations

Successfully applied to XMM-Newton and Chandra data

Quantified uncertainties in source parameters

Abstract

We present a powerful new algorithm that combines both spatial information (event locations and the point spread function) and spectral information (photon energies) to separate photons from overlapping sources. We use Bayesian statistical methods to simultaneously infer the number of overlapping sources, to probabilistically separate the photons among the sources, and to fit the parameters describing the individual sources. Using the Bayesian joint posterior distribution, we are able to coherently quantify the uncertainties associated with all these parameters. The advantages of combining spatial and spectral information are demonstrated through a simulation study. The utility of the approach is then illustrated by analysis of observations of FK Aqr and FL Aqr with the XMM-Newton Observatory and the central region of the Orion Nebula Cluster with the Chandra X-ray Observatory.