Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Random Forest Machine Learning Methods

TAIGA Collaboration

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper we present the first attempt of adaptation the Random Forest (RF) machine learning algorithm to gamma/hadron separation in the TAIGA experiment (Tunka Advanced Instrument for cosmic ray physics and Gamma-ray Astronomy). The TAIGA experiment will include HiSCORE array with 120 wide-angle Cherenkov detectors on the area of 1 km2 and 5 Imaging Atmospheric Cherenkov Telescopes (IACT) on the same area. At the first stage of the analysis, only images obtained by one IACT were included in consideration. The training process occurs on samples of parameterized images obtained from Monte Carlo (MC) data for gammas and hadrons with a ‘Scaled Hillas Parameters’ standard technique. It was shown that the program effectively separates gamma-like showers, RF method does produce stable results and is robust with respect to input parameters and provides a simple control and setup of the procedure for extracting showers from gamma rays.

Original languageEnglish
Article number008
JournalProceedings of Science
Volume410
Publication statusPublished - 12 Jan 2022
Event5th International Workshop on Deep Learning in Computational Physics, DLCP 2021 - Moscow, Russian Federation
Duration: 28 Jun 202129 Jun 2021

OECD FOS+WOS

  • 1.03 PHYSICAL SCIENCES AND ASTRONOMY

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