%0 Journal Article
%T Analyzing gamma rays of the Galactic Center with deep learning
%A Caron, S.
%A Gomez-Vargas, G. A.
%A Hendriks, L.
%A Ruiz de Austri, R.
%J Journal of Cosmology and Astroparticle Physics
%D 2018
%V 05
%N 5
%I Iop Publishing Ltd
%@ 1475-7516
%G English
%F Caron_etal2018
%O WOS:000432869300005
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=3582), last updated on Tue, 05 Jun 2018 14:59:32 +0000
%X We present the application of convolutional neural networks to a particular problem in gamma ray astronomy. Explicitly, we use this method to investigate the origin of an excess emission of GeV gamma rays in the direction of the Galactic Center, reported by several groups by analyzing Fermi-LAT data. Interpretations of this excess include gamma rays created by the annihilation of dark matter particles and gamma rays originating from a collection of unresolved point sources, such as millisecond pulsars. We train and test convolutional neural networks with simulated Fermi-LAT images based on point and diffuse emission models of the Galactic Center tuned to measured gamma ray data. Our new method allows precise measurements of the contribution and properties of an unresolved population of gamma ray point sources in the interstellar diffuse emission model. The current model predicts the fraction of unresolved point sources with an error of up to 10% and this is expected to decrease with future work.
%K gamma ray experiments
%K dark matter simulations
%R 10.1088/1475-7516/2018/05/058
%U http://arxiv.org/abs/1708.06706
%U https://doi.org/10.1088/1475-7516/2018/05/058
%P 058-24pp