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Author | HAWC Collaboration (Alfaro, R. et al); Salesa Greus, F. | ||||
Title | Gamma/hadron separation with the HAWC observatory | Type | Journal Article | ||
Year | 2022 | Publication | Nuclear Instruments & Methods in Physics Research A | Abbreviated Journal | Nucl. Instrum. Methods Phys. Res. A |
Volume | 1039 | Issue | Pages | 166984 - 13pp | |
Keywords | 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. | ||||
Address | [Alfaro, R.; Angeles Camacho, J. R.; Avila Rojas, D.; Belmont-Moreno, E.; Espinoza, C.; Garcia, D.; Hernandez, S.; Leon Vargas, H.; Sandoval, A.; Serna-Franco, J.] Univ Nacl Autonoma Mexico, Inst Fis, Mexico City, DF, Mexico, Email: tcapistran@astro.unam.mx; | ||||
Corporate Author | Thesis | ||||
Publisher | Elsevier | Place of Publication | Editor | ||
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0168-9002 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | WOS:000861747900006 | Approved | no | ||
Is ISI | yes | International Collaboration | yes | ||
Call Number | IFIC @ pastor @ | Serial | 5371 | ||
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