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

被引：7
|
作者：
Sharma, Mradul [1 ]
Nayak, Jitadeepa [2 ]
Koul, Maharaj Krishna [1 ]
Bose, Smarajit [2 ]
Mitra, Abhas [1 ]
机构：
[1] Bhabha Atom Res Ctr, Astrophys Sci Div, Bombay 400085, Maharashtra, India
[2] Indian Stat Inst, Bayesian & Interdisciplinary Res Unit, Kolkata, India
来源：
RESEARCH IN ASTRONOMY AND ASTROPHYSICS | 2014年 / 14卷 / 11期
关键词：
methods: statistical; telescopes; GAMMA-RAYS; MULTIVARIATE-ANALYSIS; CRAB-NEBULA; TEV;
D O I：
10.1088/1674-4527/14/11/012
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
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.
引用
收藏
页码：1491 / 1503
页数：13
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