Gamma-ray Trajectory Reconstruction using Deep Learning Methods

The Dark Matter Particle Explorer experiment allows for γ-ray detection up to TeV energies, with an unprecedented energy resolution of about 1%, which makes it a unique instrument for γ-ray physics at these energies. Diffuse γ-ray analysis requires a precise tracking method in order to identify the incoming particle through the different detectors, as well as defining its origin in space. A deep-learning tool for track reconstruction has already been developed for electrons and ions. In this analysis we used this tool trained on electron samples on γ-ray samples to assess its efficiency on trajectory reconstruction up to 10 TeV. Preliminary results show very promising tracking precision and bring the prospect of this new tool in high energy γ-ray study. The efficiency of the γ selection by the deep-learning method is compared to the original track of simulated events.