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Title: Identification of high-energy astrophysical point sources via hierarchical Bayesian nonparametric clustering Authors:  Andrea Sottosanti - University of Padua (Italy) [presenting]
Mauro Bernardi - University of Padova (Italy)
Alessandra Rosalba Brazzale - University of Padova (Italy)
Alex Geringer-Sameth - Imperial College London (United Kingdom)
David Stenning - Simon Fraser University (Canada)
Roberto Trotta - SISSA (Italy)
David van Dyk - Imperial College London (United Kingdom)
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 remains an open question in astrophysics. The focus is 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. The proposed Bayesian methodology uses both the spatial and energy information to identify point sources, that is, separate them from the spatially-varying background, estimate their number, and 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 Fermi Gamma-ray Space Telescope.