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 new unlabeled images towards a method for refining the classifier using real images captured by the telescopes. Our results show that one can use unlabeled observed data to increase the accuracy and general performance of current simulation-based classifiers, which suggests that continuous improvement of the learning model could be possible under real data conditions.