PoS - Proceedings of Science
Volume 395 - 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect
Use of Machine Learning for gamma/hadron separation with HAWC
Presented by T. Capistrán*, K.L. Fan, J.T. Linnemann, I. Torres, P.M. Saz Parkinson and P.L.H. Yu  on behalf of A.U. Abeysekara, A. Albert, R.J. Alfaro, C. Alvarez, J.d.D. Álvarez Romero, J.R. Angeles Camacho, J.C. Arteaga Velazquez, A.B. Kollamparambil, D.O. Avila Rojas, H.A. Ayala Solares, R. Babu, V. Baghmanyan, A.S. Barber, J. Becerra Gonzalez, E. Belmont-Moreno, S. BenZvi, D. Berley, C. Brisbois, K.S. Caballero Mora, A. Carramiñana, S. Casanova, O. Chaparro-Amaro, U. Cotti, J. Cotzomi, S. Coutiño de León, E. de la Fuente, C.L. de León, L. Diaz, R. Diaz Hernandez, J.C. Díaz Vélez, B. Dingus, M. Durocher, M. DuVernois, R. Ellsworth, K. Engel, M.C. Espinoza Hernández, K. Fang, M. Fernandez Alonso, B. Fick, H. Fleischhack, J.L. Flores, N.I. Fraija, D. Garcia Aguilar, J.A. Garcia-Gonzalez, J.L. García-Luna, G. García-Torales, F. Garfias, G. Giacinti, H. Goksu, M.M. González, J.A. Goodman, J.P. Harding, S. Hernández Cadena, I. Herzog, J. Hinton, B. Hona, D. Huang, F. Hueyotl-Zahuantitla, M. Hui, B. Humensky, P. Hüntemeyer, A. Iriarte, A. Jardin-Blicq, H. Jhee, V. Joshi, D. Kieda, G.J. Kunde, S. Kunwar, A. Lara, J. Lee, W.H. Lee, D. Lennarz, H. Leon Vargas, A.L. Longinotti, R. Lopez-Coto, G. Luis-Raya, J. Lundeen, K. Malone, V. Marandon, O.M. Martinez, I. Martinez Castellanos, H. Martínez Huerta, J. Martínez-Castro, J. Matthews, J. McEnery, P. Miranda-Romagnoli, J.A. Morales Soto, E. Moreno Barbosa, M. Mostafa, A. Nayerhoda, L. Nellen, M. Newbold, M.U. Nisa, R. Noriega-Papaqui, L. Olivera-Nieto, N. Omodei, A. Peisker, Y. Pérez Araujo, E.G. Pérez Pérez, C.D. Rho, C. Rivière, D. Rosa-Gonzalez, E. Ruiz-Velasco, J. Ryan, H.I. Salazar, F. Salesa Greus, A. Sandoval, M. Schneider, H. Schoorlemmer, J. Serna-Franco, G. Sinnis, A.J. Smith, W.R. Springer, P. Surajbali, I. Taboada, M. Tanner, K. Tollefson, R. Torres Escobedo, R.M. Turner, F.J. Urena Mena, L. Villaseñor, X. Wang, I.J. Watson, T. Weisgarber, F. Werner, E.J. Willox, J. Wood, G. Yodh, A. Zepeda and H. Zhouet al. (click to show)
Full text: pdf
Pre-published on: July 23, 2021
Published on: March 18, 2022
Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
DOI: https://doi.org/10.22323/1.395.0745
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