The search of gamma-ray sources in the extra-galactic space is one of the main targets of the Fermi telescope project, which aims to identify and study the nature of high energy phenomena in the universe. Starting from a collection of photons, we perform an unsupervised analysis using a Bayesian mixture model with an unknown number of components to determine the number of gamma ray sources in the map. The parameters of the model are estimated using a reversible jump MCMC algorithm. We finally propose a new method which exploits the distributions of both the weights of the mixture components and the energy spectra to qualify the nature of each cluster.

Bayesian Mixture Models for the Detection of High-Energy Astronomical Sources

SOTTOSANTI, ANDREA
;
Denis Bastieri;Alessandra R. Brazzale
2017

Abstract

The search of gamma-ray sources in the extra-galactic space is one of the main targets of the Fermi telescope project, which aims to identify and study the nature of high energy phenomena in the universe. Starting from a collection of photons, we perform an unsupervised analysis using a Bayesian mixture model with an unknown number of components to determine the number of gamma ray sources in the map. The parameters of the model are estimated using a reversible jump MCMC algorithm. We finally propose a new method which exploits the distributions of both the weights of the mixture components and the energy spectra to qualify the nature of each cluster.
2017
Proceedings of the SIS 2017 Statistical Conference "Statistics and Data Science: new challenges, new generations"
978-88-6453-521-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3261911
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