Application of convolutional neural networks for extensive air shower separation in the SPHERE-3 experiment

TL;DR

This paper explores the use of convolutional neural networks to improve the classification of extensive air shower images in the SPHERE-3 experiment, enhancing background separation for cosmic ray studies.

Contribution

It introduces a CNN-based approach for air shower image classification, demonstrating its effectiveness compared to traditional trigger systems in the SPHERE-3 detector.

Findings

CNN outperforms traditional trigger system in background separation

Simulated detector response validates CNN effectiveness

Improved event classification accuracy in cosmic ray detection

Abstract

A new SPHERE-3 telescope is being developed for cosmic rays spectrum and mass composition studies 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. Detector response simulations include photons tracing through the optical system, silicon photomultiplier operation and electronics response and digitization process. The results are compared to the SPHERE-2 trigger system performance.