arXiv · 2506.18277
HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Abstract
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, 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 Large High Altitude Air Shower Observatory (LHAASO) and the upcoming Southern Wide-field Gamma-ray Observatory (SWGO).
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R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. 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., Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez 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, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León. 2025-06-23. HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation. https://arxiv.org/abs/2506.18277
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