@Article{HAWCCollaborationAlfaro+SalesaGreus2022,
author="HAWC Collaboration (Alfaro, R. et al
and Salesa Greus, F.",
title="Gamma/hadron separation with the HAWC observatory",
journal="Nuclear Instruments {\&} Methods in Physics Research A",
year="2022",
publisher="Elsevier",
volume="1039",
pages="166984 - 13pp",
optkeywords="High energy; Crab Nebula; G/H separation; Machine Learning",
abstract="The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority (> 99.9{\%}) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.",
optnote="WOS:000861747900006",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5371), last updated on Mon, 17 Oct 2022 08:52:02 +0000",
issn="0168-9002",
doi="10.1016/j.nima.2022.166984",
opturl="https://arxiv.org/abs/2205.12188",
opturl="https://doi.org/10.1016/j.nima.2022.166984",
language="English"
}