Deep Learning Semi-Supervised Strategy for Gamma/Hadron Classification of Imaging Atmospheric Cherenkov Telescope Events

Diego Riquelme, Mauricio Araya, Sebastian Borquez, Boris Panes, Edson Carquin

Abstract: The new Cherenkov Telescope Array (CTA) will record astrophysical gamma-ray events with an energy coverage range, angular resolution, and flux sensitivity never achieved before. The Earth’s atmosphere produces Cherenkov’s light when a shower of particles is induced by a high-energy particle of astrophysical origin (gammas, hadrons, electrons, etc.). The energy and direction of these gamma air shower events can be reconstructed stereoscopically using imaging atmospheric Cherenkov detectors. Since most of CTA’s scientific goals focus on identifying and studying Gamma-Ray sources, it is imperative to distinguish this specific type of event from the hadronic cosmic ray background with the highest possible efficiency. Following this objective, we designed a competitive deep-learning-based approach for gamma/background classification. First, we train the model with simulated images in a standard supervised fashion. Then, we explore a novel self-supervised approach that allows the use of ne