# Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees

## TL;DR

This paper demonstrates that using boosted decision trees significantly improves gamma/hadron separation in Cherenkov telescope data, enhancing the sensitivity to faint gamma-ray sources.

## Contribution

The study applies boosted decision trees to VERITAS data, showing improved gamma/hadron discrimination over standard analysis methods.

## Key findings

- Enhanced sensitivity to faint gamma-ray sources.
- Better background suppression using machine learning.
- Improved gamma/hadron separation performance.

## Abstract

Imaging atmospheric Cherenkov telescopes record an enormous number of cosmic-ray background events. Suppressing these background events while retaining $\gamma$-rays is key to achieving good sensitivity to faint $\gamma$-ray sources. The differentiation between signal and background events can be accomplished using machine learning algorithms, which are already used in various fields of physics. Multivariate analyses combine several variables into a single variable that indicates the degree to which an event is $\gamma$-ray-like or cosmic-ray-like. In this paper we will focus on the use of boosted decision trees for $\gamma$/hadron separation. We apply the method to data from the Very Energetic Radiation Imaging Telescope Array System (VERITAS), and demonstrate an improved sensitivity compared to the VERITAS standard analysis.