Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks

TL;DR

This paper introduces a novel background rejection algorithm for atmospheric Cherenkov telescopes using recurrent and convolutional neural networks, significantly improving performance over existing methods in gamma-ray astronomy.

Contribution

The study applies advanced neural network techniques to enhance background rejection in Cherenkov telescopes, demonstrating improved results on real observational data.

Findings

20-25% reduction in background rate with RNNs

CNN performance varies with sky brightness

Significant improvement over standard methods

Abstract

In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of the latest techniques in machine learning, namely recurrent and convolutional neural networks, to the background rejection problem. Use of these machine-learning techniques addresses some of the key challenges encountered in the currently implemented algorithms and helps to significantly increase the background rejection performance at all energies. We apply these machine learning techniques to the H.E.S.S. telescope array, first testing their performance on simulated data and then applying the analysis to two well known gamma-ray sources. With real observational data we find significantly improved performance over the current standard methods, with a 20-25\% reduction in the background rate when applying the recurrent neural network analysis. Importantly, we also find that the convolutional neural network results are strongly dependent on the sky brightness in the source region which has important implications for the future implementation of this method in Cherenkov telescope analysis.