Discriminating sub-TeV gamma and hadron-induced showers through their footprints

Abstract

Gamma/hadron discrimination in ground-based gamma-ray observatories at the sub-TeV energy range is challenging as traditional muon-based methods become less effective at lower energies. This work explores a novel gamma/hadron discrimination method for extensive air shower arrays that analyzes the shower signal footprint patterns using a state-of-the-art pretrained vision transformer. The resilience of the method to background noise, such as atmospheric muons and low-energy proton showers, along with its adaptability to different zenith angles and array configurations, demonstrates its potential for application in current and future ground-based gamma-ray observatories.