Cosmic-ray energy reconstruction using machine learning techniques

Alvarado A, Capistrán T, Torres I, Sacahuí JR, Alfaro R, Albert A, Alvarez C, Andrés A, Arteaga-Velázquez JC, Avila Rojas D, Ayala Solares HA, Babu R, Belmont-Moreno E, Caballero-Mora KS, Yun-Cárcamo S, Carramiñana A, Carreón F, Cotti U, Cotzomi J, Coutiño de León S, De la Fuente E, Depaoli D, de León C, Diaz Hernandez R, Díaz-Vélez JC, Dingus BL, Durocher M, DuVernois MA, Engel K, Espinoza C, Fan KL, Fang K, Fraija NI, García-González JA, Garfias F, Goksu H, González MM, Goodman JA, Groetsch S, Harding JP, Hernandez S, Herzog I, Hinton J, Huang D, Hueyotl-Zahuantitla F, Hüntemeyer P, Iriarte A, Joshi V, Kaufmann S, Kieda D, Lara A, Lee J, Lee WH, León Vargas H, Linnemann J, Longinotti AL, Luis-Raya G, Malone K, Martínez-Castro J, Matthews JA, Miranda-Romagnoli P, Montes J, Morales-Soto JA, Mostafá M, Nellen L, Nisa MU, Noriega-Papaqui R, Olivera-Nieto L, Omodei N, Pérez Araujo Y, Pérez-Pérez EG, Pratts A, Rho CD, Rosa-Gonzalez D, Ruiz-Velasco E, Salazar H, Salazar-Gallegos D, Sandoval A, Schneider M, Schwefer G, Serna-Franco J, Smith AJ, Son Y, Springer RW, Tibolla O, Tollefson K, Torres-Escobedo R, Turner R, Ureña-Mena F, Varela E, Villaseñor L, Wang X, Watson IJ, Werner F, Whitaker K, Willox E, Wu H, Zhou H (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Publisher: Sissa Medialab Srl

Book Volume: 444

Conference Proceedings Title: Proceedings of Science

Event location: Nagoya, JPN

Abstract

HAWC is a ground-based observatory consisting of 300 water Cherenkov detectors, which observes the extensive air showers induced by cosmic rays from some TeV to a few PeV and, in particular, gamma rays from 300 GeV to more than 100 TeV. One of the crucial features required for a detector of extensive air showers is the estimation of the primary energy of the events to study the spectra of cosmic and gamma rays. For HAWC there are currently two gamma-ray energy estimators: one relies on a ground density parameter, while the other utilizes an artificial neural network. For the cosmic ray energy estimation, there is only one estimator based on maximum likelihood procedures and measurements of the lateral charge distribution of the events. It is worthwhile to update the cosmic-ray energy estimator due to recent improvements of the extensive air shower offline-reconstruction techniques in HAWC. Therefore, we implemented an artificial neural network to reconstruct the primary energy of hadronic events trained with several observables that characterize the air showers. We trained several models and evaluated their performance against the existing cosmic ray energy estimator. In this work, we present the features and performance of these models.

Authors with CRIS profile

Involved external institutions

National Autonomous University of Mexico / Universidad Nacional Autónoma de México (UNAM) Mexico (MX) National Institute of Astrophysics, Optics and Electronics / Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) Mexico (MX) University of San Carlos of Guatemala (USAC) / Universidad de San Carlos de Guatemala Guatemala (GT) Los Alamos National Laboratory United States (USA) (US) Universidad Autónoma de Chiapas (UNACH) Mexico (MX) Universidad Michoacana de San Nicolás de Hidalgo (UMSNH) Mexico (MX) University of Maryland United States (USA) (US) Benemérita Universidad Autónoma de Puebla (BUAP) / Meritorious Autonomous University of Puebla Mexico (MX) University of Wisconsin - Madison United States (USA) (US) Universidad de Guadalajara (UDEG) Mexico (MX) Max-Planck-Institut für Kernphysik (MPIK) / Max Planck Institute for Nuclear Physics Germany (DE) Stanford University United States (USA) (US) Universidad Politécnica de Pachuca (UPP) Mexico (MX) Sungkyunkwan University (SKKU) Korea, Republic of (KR) Michigan Technological University United States (USA) (US) University of Utah United States (USA) (US) University of Seoul (UOS) / 서울시립대학교 Korea, Republic of (KR) Tecnológico de Monterrey (ITESM) Mexico (MX) Michigan State University United States (USA) (US) Shanghai Jiao Tong University / 上海交通大学 China (CN) Pennsylvania State University (Penn State) United States (USA) (US) Autonomous University of the State of Hidalgo / Universidad Autónoma del Estado de Hidalgo Mexico (MX) National Polytechnic Institute of Mexico / Instituto Politécnico Nacional de México Mexico (MX) University of New Mexico (UNM) / Universidad de Nuevo México United States (USA) (US)

How to cite

APA:

Alvarado, A., Capistrán, T., Torres, I., Sacahuí, J.R., Alfaro, R., Albert, A.,... Zhou, H. (2024). Cosmic-ray energy reconstruction using machine learning techniques. In Proceedings of Science. Nagoya, JPN: Sissa Medialab Srl.

MLA:

Alvarado, A., et al. "Cosmic-ray energy reconstruction using machine learning techniques." Proceedings of the 38th International Cosmic Ray Conference, ICRC 2023, Nagoya, JPN Sissa Medialab Srl, 2024.

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