Use of Machine Learning for gamma/hadron separation with HAWC

TL;DR

This paper investigates machine learning methods, specifically Boosted Decision Trees and Neural Networks, to improve gamma/hadron separation in the HAWC observatory, demonstrating enhanced background rejection over existing techniques.

Contribution

It introduces machine learning techniques for gamma/hadron separation in HAWC, surpassing the current single-variable cut method.

Findings

Improved background rejection in HAWC data

Enhanced gamma/hadron separation performance

Validation on Crab nebula data

Abstract

Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.