TY  - JOUR
AU  - Caron, S.
AU  - Gomez-Vargas, G. A.
AU  - Hendriks, L.
AU  - Ruiz de Austri, R.
PY  - 2018
DA  - 2018//
TI  - Analyzing gamma rays of the Galactic Center with deep learning
T2  - J. Cosmol. Astropart. Phys.
JO  - Journal of Cosmology and Astroparticle Physics
SP  - 058
EP  - 24pp
VL  - 05
IS  - 5
PB  - Iop Publishing Ltd
KW  - gamma ray experiments
KW  - dark matter simulations
AB  - 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.
SN  - 1475-7516
UR  - http://arxiv.org/abs/1708.06706
UR  - https://doi.org/10.1088/1475-7516/2018/05/058
DO  - 10.1088/1475-7516/2018/05/058
LA  - English
N1  - WOS:000432869300005
ID  - Caron_etal2018
ER  -