Application of deep learning methods to high-energy astrophysics

High-energy gamma-ray astronomy, at frequencies of 100 MeV to 100 GeV, yields insights into the fields of compact objects, extreme processes, and particle propagation. Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope, many without any known counterpart at other wavelengths or clear identification of the source. Deep learning algorithms have been successfully applied to a variety of problems in astronomy. In this paper, I give some typical examples for classifying Fermi sources with deep learning methods, to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.