Gamma/Hadron Separation with the HAWC Observatory

• Published in: arXiv.org 2022-05
• Main Authors: Alfaro, R, Alvarez, C, Álvarez, J D, Angeles Camacho, J R, Arteaga-Velázquez, J C, D Avila Rojas, Ayala Solares, H A, Babu, R, Belmont-Moreno, E, Brisbois, C, Caballero-Mora, K S, Capistrán, T, Carramiñana, A, Casanova, S, Chaparro-Amaro, O, Cotti, U, Cotzomi, J, S Coutiño de León, De la Fuente, E, de León, C, R Diaz Hernandez, Dingus, B L, DuVernois, M A, Durocher, M, Díaz-Vélez, J C, Ellsworth, R W, Engel, K, Espinoza, C, Fan, K L, M Fernández Alonso, Fraija, N, Garcia, D, García-González, J A, Garfias, F, González, M M, Goodman, J A, Harding, J P, Hernandez, S, Hona, B, Huang, D, Hueyotl-Zahuantitla, F, Hüntemeyer, P, Iriarte, A, Jardin-Blicq, A, Joshi, V, Kaufmann, S, G J Kundem A Lara, Lee, W H, Lee, J, H León Vargas, Linnemann, J T, Luis-Raya, G, Lundeen, J, Malone, K, Marandon, V, Martinez, O, Martínez-Castro, J, Matthews, J A, Miranda-Romagnoli, P, Morales-Soto, J A, Nayerhoda, A, Nellen, L, Nisa, M U, Noriega-Papaqui, R, Olivera-Nieto, L, Omodei, N, Peisker, A, Y Pérez Araujo, Pérez-Pérez, E G, Rho, C D, Rosa-González, D, Ruiz-Velasco, E, Salazar, H, F Salesa Greus, Sandoval, A, Saz Parkinson, P M, Serna-Franco, J, Smith, A J, Springer, R W, Tibolla, O, Tollefson, K, Torres, I, Torres-Escobedo, R, Turner, R, Ureña-Mena, F, Villaseñor, L, Wang, X, Watson, I J, Werner, F, Willox, E, Wood, J, Zepeda, A, Zhou, H
• Format: Article
• Language: English
• Subjects: Cosmic rays; Decision trees; Gamma ray detectors; Gamma ray sources; Gamma rays; Hadrons; High altitude; Machine learning; Neural networks; Observatories; Separation; Showers
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2205.12188

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.