PoS - Proceedings of Science
Volume 395 - 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect
Convolutional Neural Networks for Low Energy Gamma-Ray Air Shower Identification with HAWC
I. Watson*,  Hawc Collaboration, A.U. Abeysekara, A. Albert, R. 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, T. Capistrán, 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, J. Fan, 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, J. Linnemann, 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, I. Torres, 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 12, 2021
Published on: March 18, 2022

A major task in ground-based gamma-ray astrophysics analyses is to
separate events caused by gamma rays from the overwhelming hadronic
cosmic-ray background. In this talk we are interested in improving
the gamma ray regime below 1 TeV, where the gamma and cosmic-ray
separation becomes more difficult. Traditionally, the separation has
been done in particle sampling arrays by selections on summary
variables which distinguish features between the gamma and
cosmic-ray air showers, though the distributions become more similar
with lower energies. The structure of the HAWC observatory, however,
makes it natural to interpret the charge deposition collected by the
detectors as pixels in an image, which makes it an ideal case for
the use of modern deep learning techniques, allowing for good
performance classifers produced directly from low-level detector
DOI: https://doi.org/10.22323/1.395.0770
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