Identification of high-energy astrophysical point sources via hierarchical Bayesian nonparametric clustering

TL;DR

This paper introduces a Bayesian nonparametric clustering method that uses spatial and energy data to identify and characterize high-energy astrophysical point sources amidst complex backgrounds.

Contribution

It develops a novel hierarchical Bayesian approach combining Dirichlet process mixtures and B-spline background modeling for source detection in high-energy astrophysics.

Findings

Accurately identifies point sources in simulated data.

Effectively separates sources from background in real Fermi telescope data.

Provides posterior probabilities for photon-source associations.

Abstract

The light we receive from distant astrophysical objects carries information about their origins and the physical mechanisms that power them. The study of these signals, however, is complicated by the fact that observations are often a mixture of the light emitted by multiple localized sources situated in a spatially-varying background. A general algorithm to achieve robust and accurate source identification in this case remains an open question in astrophysics. This paper focuses on high-energy light (such as X-rays and gamma-rays), for which observatories can detect individual photons (quanta of light), measuring their incoming direction, arrival time, and energy. Our proposed Bayesian methodology uses both the spatial and energy information to identify point sources, that is, separate them from the spatially-varying background, to estimate their number, and to compute the posterior probabilities that each photon originated from each identified source. This is accomplished via a Dirichlet process mixture while the background is simultaneously reconstructed via a flexible Bayesian nonparametric model based on B-splines. Our proposed method is validated with a suite of simulation studies and illustrated with an application to a complex region of the sky observed by the \emph{Fermi} Gamma-ray Space Telescope.