Application of Convolutional Neural Networks for Extensive Air Shower Separation in the SPHERE-3 Experiment

Abstract

A new SPHERE-3 telescope is being developed for the study of the cosmic ray spectrum and mass composition in the 5–1000 PeV energy range. Registration of extensive air showers using reflected Cherenkov light method applied in the SPHERE detector series requires a good trigger system for accurate separation of events from the background produced by starlight and airglow photons reflected from the snow. Here, we present the results of convolutional networks application for the classification of images obtained from Monte Carlo simulation of the detector. The simulated detector response includes photon tracing through the optical system, silicon photomultiplier operation, and the electronics response and digitization process. The results are compared to the SPHERE-2 trigger system performance.