Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC

TL;DR

This paper details the application of the Random Forest machine learning method to improve gamma/hadron separation in data from the MAGIC gamma-ray telescope, demonstrating its advantages over traditional techniques.

Contribution

It introduces the use of Random Forest for gamma/hadron discrimination and continuous parameter estimation in Cherenkov telescope data analysis.

Findings

RF outperforms traditional semi-empirical methods

Effective for both classification and continuous parameter estimation

Discusses implementation challenges and solutions

Abstract

The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to be discriminated against a dominating background of hadronic cosmic-ray particles. We describe the application of RF for this gamma/hadron separation. The RF method often shows superior performance in comparison with traditional semi-empirical techniques. Critical issues of the method and its implementation are discussed. An application of the RF method for estimation of a continuous parameter from related variables, rather than discrete classes, is also discussed.