HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation Article Swipe

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R. Alfaro , C. Álvarez , Amado Andrés , E. Anita-Rangel , Miguel Araya , J. C. Arteaga-Velázquez , D. Avila Rojas , H. A. Ayala Solares , Rishi Babu , P. Bangale , E. Belmont‐Moreno , A. Bernal , T. Capistrán , A. Carramiñana , F. Carreón , S. Casanova , U. Cotti , E. De la Fuente , Davide Depaoli , P. Desiati , Niccolò Di Lalla , R. Diaz Hernandez , M. A. DuVernois , J. C. Díaz-Vélez , K. Engel , T. Ergin , C. Espinoza , Kwok Lung Fan , N. Fraija , S. Fraija , J. A. García-González , F. Garfias , Nilanjana Ghosh , A. Gonzalez Muñoz , M. M. González , Jorge A. González , J. A. Goodman , S. Groetsch , J. P. Harding , Sergio Hernández Cadena , I. Herzog , Dezhi Huang , P. Hüntemeyer , A. Iriarte , S. Kaufmann , D. Kieda , Kathryn Leavitt , William H. Lee , H. León Vargas , J. Linnemann , A. L. Longinotti , G. Luis-Raya , K. Malone , O. Martı́nez , J. Martínez-Castro , John A. Matthews , P. Miranda-Romagnoli , P. E. Mirón-Enriquez , J. A. Montes , J. A. Morales-Soto , E. Moreno , M. Najafi , A. Nayerhoda , L. Nellen , N. Omodei , M. R. Zapatero Osorio , E. Ponce , Y. Pérez Araujo , E. G. Pérez‐Pérez , Chang Dong Rho , A. Parra , D. Rosa‐González , M. Roth , H. Salazar , A. Sandoval , J. Serna-Franco , A. J. Smith , Y. Son , R. W. Springer , O. Tibolla , K. Tollefson , I. Torres , R. Torres-Escobedo , Enrique Varela , L. Villaseñor , X. Wang , Z. Wang , I. J. Watson , H. Wu , S. Yu , Hao Zhou , C. de León ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.3847/1538-4357/ae0186

Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With more than a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich data set, we introduce a machine learning approach for gamma-hadron separation. A multilayer perceptron shows the best performance, surpassing traditional and other machine learning–based methods. This approach shows a notable improvement in the detector’s sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as the Large High Altitude Air Shower Observatory and the upcoming Southern Wide-field Gamma-ray Observatory.

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Type: article
Language: en
Landing Page: https://doi.org/10.3847/1538-4357/ae0186
OA Status: gold
References: 26
OpenAlex ID: https://openalex.org/W4415030121

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DOI: https://doi.org/10.3847/1538-4357/ae0186

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Title: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation

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Publication year: 2025

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Publication date: 2025-10-10

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Authors: R. Alfaro, C. Álvarez, Amado Andrés, E. Anita-Rangel, Miguel Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, Rishi Babu, P. Bangale, E. Belmont‐Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, Davide Depaoli, P. Desiati, Niccolò Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, Kwok Lung Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, Nilanjana Ghosh, A. Gonzalez Muñoz, M. M. González, Jorge A. González, J. A. Goodman, S. Groetsch, J. P. Harding, Sergio Hernández Cadena, I. Herzog, Dezhi Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, Kathryn Leavitt, William H. Lee, H. León Vargas, J. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martı́nez, J. Martínez-Castro, John A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. R. Zapatero Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez‐Pérez, Chang Dong Rho, A. Parra, D. Rosa‐González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, Enrique Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, Hao Zhou, C. de León

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