Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method

TL;DR

This paper applies a multivariate classification method to improve background reduction in gamma-ray astronomy data from IACTs, enhancing the sensitivity of the H.E.S.S. telescope system.

Contribution

It introduces a tree classification approach for gamma-hadron separation, demonstrating improved background rejection over standard methods.

Findings

Enhanced background reduction in H.E.S.S. data

Stable performance of the classification method

Potential for increased sensitivity in gamma-ray detection

Abstract

In recent years, Imaging Atmospheric Cherenkov Telescopes (IACTs) have discovered a rich diversity of very high energy (VHE, > 100 GeV) gamma-ray emitters in the sky. These instruments image Cherenkov light emitted by gamma-ray induced particle cascades in the atmosphere. Background from the much more numerous cosmic-ray cascades is efficiently reduced by considering the shape of the shower images, and the capability to reduce this background is one of the key aspects that determine the sensitivity of a IACT. In this work we apply a tree classification method to data from the High Energy Stereoscopic System (H.E.S.S.). We show the stability of the method and its capabilities to yield an improved background reduction compared to the H.E.S.S. Standard Analysis.