Vol 14, No 11 (2014) / Sharma

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

Mradul Sharma, Jitadeepa Nayak, Maharaj Krishna Koul, Smarajit Bose, Abhas Mitra

Abstract

A detailed case study of γ-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 γ-hadron segregation.

Keywords


methods: statistical — telescopes

Full Text:

PDF


DOI: https://doi.org/10.1088/1674-4527/14/11/012

Refbacks

  • There are currently no refbacks.