Optimization of multivariate analysis for IACT stereoscopic systems

TL;DR

This paper introduces an optimized multivariate analysis method using boosted decision trees to improve gamma-ray event discrimination in IACT stereoscopic systems, significantly enhancing sensitivity over traditional methods.

Contribution

It proposes a boosted decision tree approach to combine multiple reconstruction methods, achieving better signal-background separation in gamma-ray astronomy.

Findings

Increased signal over background ratio with the new method

Enhanced sensitivity demonstrated on benchmark sources

Outperforms standard analysis approaches

Abstract

Multivariate methods have been recently introduced and successfully applied for the discrimination of signal from background in the selection of genuine very-high energy gamma-ray events with the H.E.S.S. Imaging Atmospheric Cerenkov Telescope. The complementary performance of three independent reconstruction methods developed for the H.E.S.S. data analysis, namely Hillas, model and 3D-model suggests the optimization of their combination through the application of a resulting efficient multivariate estimator. In this work the boosted decision tree method is proposed leading to a significant increase in the signal over background ratio compared to the standard approaches. The improved sensitivity is also demonstrated through a comparative analysis of a set of benchmark astrophysical sources.