Gamma/hadron separation with the HAWC observatory

R. Alfaro, C. Alvarez, J. D. Álvarez, J. R. Angeles Camacho, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, E. Belmont-Moreno, C. Brisbois, K. S. Caballero-Mora, T. Capistrán, A. Carramiñana, S. Casanova, O. Chaparro-Amaro, U. Cotti, J. Cotzomi, S. Coutiño de León, E. De la Fuente, C. de LeónR. Diaz Hernandez, B. L. Dingus, M. A. DuVernois, M. Durocher, J. C. Díaz-Vélez, R. W. Ellsworth, K. Engel, C. Espinoza, K. L. Fan, M. Fernández Alonso, N. Fraija, D. Garcia, J. A. García-González, F. Garfias, M. M. González, J. A. Goodman, J. P. Harding, S. Hernandez, B. Hona, D. Huang, F. Hueyotl-Zahuantitla, P. Hüntemeyer, A. Iriarte, A. Jardin-Blicq, V. Joshi, S. Kaufmann, G. J. Kunde, A. Lara, W. H. Lee, J. Lee, H. León Vargas, J. T. Linnemann, G. Luis-Raya, J. Lundeen, K. Malone, V. Marandon, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, J. A. Morales-Soto, A. Nayerhoda, L. Nellen, M. U. Nisa, R. Noriega-Papaqui, L. Olivera-Nieto, N. Omodei, A. Peisker, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, D. Rosa-González, E. Ruiz-Velasco, H. Salazar, F. Salesa Greus, A. Sandoval, P. M. Saz Parkinson, J. Serna-Franco, A. J. Smith, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, R. Turner, F. Ureña-Mena, L. Villaseñor, X. Wang, I. J. Watson, F. Werner, E. Willox, J. Wood, A. Zepeda, H. Zhou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Keywords

  • Crab Nebula
  • G/H separation
  • High energy
  • Machine Learning

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