eBASCS: Disentangling Overlapping Astronomical Sources II, using Spatial, Spectral, and Temporal Information
Antoine D. Meyer (1), David A. van Dyk (1), Vinay L. Kashyap (2), Luis, F. Campos (3), David E. Jones (4), Aneta Siemiginowska (2), Andreas Zezas (2, and 5) ((1) Imperial College London, Statistics Section, Department of, Mathematics, (2) Center for Astrophysics

TL;DR
This paper introduces a Bayesian method for disentangling overlapping X-ray sources by leveraging spatial, spectral, and temporal data, improving event allocation accuracy in crowded fields.
Contribution
The novel Bayesian approach incorporates temporal information to enhance source separation and parameter estimation in crowded X-ray observations.
Findings
Temporal data improves event disambiguation by up to 65%.
Method successfully separates sources in crowded fields with overlapping PSFs.
Application to real data demonstrates effective removal of contamination and detection of spectral variability.
Abstract
The analysis of individual X-ray sources that appear in a crowded field can easily be compromised by the misallocation of recorded events to their originating sources. Even with a small number of sources, that nonetheless have overlapping point spread functions, the allocation of events to sources is a complex task that is subject to uncertainty. We develop a Bayesian method designed to sift high-energy photon events from multiple sources with overlapping point spread functions, leveraging the differences in their spatial, spectral, and temporal signatures. The method probabilistically assigns each event to a given source. Such a disentanglement allows more detailed spectral or temporal analysis to focus on the individual component in isolation, free of contamination from other sources or the background. We are also able to compute source parameters of interest like their locations,…
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