Optimization of multivariate analysis for IACT stereoscopic systems

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.

10 pages, 8 figures, 3 tables, accepted for publication in Astroparticle Physics

References in corpus (4)

• Observations of the Crab Nebula with H.E.S.S
• A high performance likelihood reconstruction of gamma-rays for Imaging Atmospheric Cherenkov Telescopes
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• Upgrading and testing the 3D reconstruction of gamma-ray air showers as observed with an array of Imaging Atmospheric Cherenkov telescopes

Cited by in corpus (10)

• Design Concepts for the Cherenkov Telescope Array
• A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes
• First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning
• Handling Systematic Uncertainties and Combined Source Analyses for Atmospheric Cherenkov Telescopes
• Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads
• Validation of a new background discrimination method for the TACTIC TeV $\gamma$-ray telescope with Markarian 421 data
• Statistical Tools for Imaging Atmospheric Cherenkov Telescopes
• The Galactic Sky seen by H.E.S.S
• A new method of reconstructing very-high-energy gamma-ray spectra: the Template Background Spectrum
• Nonparametric signal separation in very-high-energy gamma ray observations with probabilistic neural networks