Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks

TL;DR

This paper explores the use of convolutional neural networks to analyze Cherenkov telescope images from the TAIGA experiment, aiming to improve gamma-ray event identification and energy estimation.

Contribution

It introduces CNN-based methods for analyzing multi-telescope Cherenkov images, comparing single and multi-telescope input performance.

Findings

CNNs improve gamma-ray event classification accuracy.

Multi-telescope images enhance energy estimation precision.

The approach demonstrates potential for real-time analysis in gamma-ray astronomy.

Abstract

Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by gamma rays and by hadrons and to infer the parameters of the event such as the energy of the primary particle. We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of the TAIGA experiment. The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays. We compare performance of the CNNs using images from a single telescope and the CNNs using images from two telescopes as inputs.