Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

TL;DR

This paper compares multiple machine learning techniques for gamma/hadron separation in atmospheric Cherenkov telescopes, finding that Random Forest is the most effective method among those tested.

Contribution

The study provides a comprehensive comparison of supervised machine learning methods for gamma/hadron segregation, highlighting the superior performance of Random Forest in this context.

Findings

Random Forest outperforms other machine learning methods.

Machine learning methods improve gamma/hadron segregation sensitivity.

The study validates simulation results with Cherenkov telescope data.

Abstract

A detailed case study of $\gamma$-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers,Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for $\gamma$-hadron segregation.