The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks

TL;DR

This paper explores the use of convolutional neural networks to analyze TAIGA-IACT Cherenkov telescope images, improving particle identification and energy reconstruction, especially with stereoscopic data.

Contribution

It demonstrates the effectiveness of CNNs for primary particle identification and energy reconstruction in TAIGA-IACT data, with improvements in stereoscopic observations.

Findings

CNNs effectively identify primary cosmic ray particles.

Energy reconstruction of gamma-rays is improved.

Stereoscopic observations enhance analysis accuracy.

Abstract

The imaging Cherenkov telescopes TAIGA-IACT, located in the Tunka valley of the republic Buryatia, accumulate a lot of data in a short period of time which must be efficiently and quickly analyzed. One of the methods of such analysis is the machine learning, which has proven its effectiveness in many technological and scientific fields in recent years. The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT: the identification of the primary particle of cosmic rays and reconstruction their physical parameters. In the work the method of Convolutional Neural Networks (CNN) was applied to process and analyze Monte-Carlo events simulated with CORSIKA. Also various CNN architectures for the processing were considered. It has been demonstrated that this method gives good results in the determining the type of primary particles of Extensive Air Shower (EAS) and the reconstruction of gamma-rays energy. The results are significantly improved in the case of stereoscopic observations.