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

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.