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

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.

Accepted to RAA

References in corpus (4)

• Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• Optimization of multivariate analysis for IACT stereoscopic systems
• Simulation studies for optimizing the trigger generation criteria for the TACTIC telescope

Cited by in corpus (4)

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