Göttingen 2025 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 57: Gamma Astronomy I

T 57.7: Vortrag

Mittwoch, 2. April 2025, 17:45–18:00, VG 3.101

Bayesian approach to signal estimation in gamma-ray astronomy with Gammapy — •Matheus Genaro Dantas Xavier, Rodrigo Guedes Lang, Tim Unbehaun, and Stefan Funk — Erlangen Centre for Astroparticle Physics (ECAP), Friedrich-Alexander-Universität Erlangen-Nürnberg

Gamma-ray observations from Imaging Atmospheric Cherenkov Telescopes, such as H.E.S.S., are overwhelmingly dominated by a background of cosmic rays. To properly estimate the strength of the observed signal, gamma-hadron separation methods are used in conjunction to background estimation techniques, where selection cuts remove the majority of background events (inevitably loosing a fraction of the unknown signal). We are interested in applying and extending a Bayesian method to perform signal estimation - the BASiL method from D'Amico et al. (2021) - to H.E.S.S. data, in both 1-dimensional (data binned in energy) and 3-dimensional (data binned in energy and spatial coordinates) analyses. This approach utilizes all available information after event reconstruction and the probability distributions associated to gamma- and hadron-like events without selection cuts. In the Bayesian framework, the posterior probability of the signal is obtained, from which credible intervals can be computed and the probability of two competing hypotheses (source or non-source) can be assessed through the Bayes factor. From simulated data, improved precision in signal reconstruction is achieved, while flux points are obtained from a modified version of Gammapy, revealing that fluxes can be measured even in highly background-dominated datasets.

Keywords: Gamma rays; Imaging Atmospheric Cherenkov Telescopes; Bayesian analysis; Gammapy; Signal estimation