Gamma/hadron separation in the TAIGA experiment with neural network methods

TL;DR

This paper investigates neural network methods for gamma/hadron separation in the TAIGA experiment, demonstrating improved detection significance of the Crab Nebula over standard methods in Cherenkov telescope data.

Contribution

It introduces neural network techniques for gamma/hadron separation in Cherenkov telescope images, showing enhanced detection significance compared to traditional Hillas parameter cuts.

Findings

Neural network methods achieved over 5.5σ significance in Crab Nebula detection.

Compared neural network approach with standard Hillas parameter cuts.

Demonstrated improved gamma-ray signal extraction in TAIGA data.

Abstract

In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongly prevails (ratio up to {10^4} over the gamma radiation flux from a point source). This ratio is valid for the Crab Nebula in the TeV energy range, since the Crab is a well-studied source for calibration and test of various methods and installations in gamma astronomy. The part of TAIGA experiment which includes three Imaging Atmospheric Cherenkov Telescopes observes this gamma-source too. Cherenkov telescopes obtain images of Extensive Air Showers. Hillas parameters can be used to analyse images in standard processing method, or images can be processed with convolutional neural networks. In this work we would like to describe the main steps and results obtained in the gamma/hadron separation task from the Crab Nebula with neural network methods. The results obtained are compared with standard processing method applied in the TAIGA collaboration and using Hillas parameter cuts. It is demonstrated that a signal was received at the level of higher than 5.5{\sigma} in 21 hours of Crab Nebula observations after processing the experimental data with the neural network method.