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).
Keywords
Cite
@article{arxiv.2506.18277,
title = {HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation},
author = {R. Alfaro and C. Alvarez and A. Andrés and E. Anita-Rangel and M. Araya and J. C. Arteaga-Velázquez and D. Avila Rojas and H. A. Ayala Solares and R. Babu and P. Bangale and E. Belmont-Moreno and A. Bernal and T. Capistrán and A. Carramiñana and F. Carreón and S. Casanova and U. Cotti and E. De la Fuente and D. Depaoli and P. Desiati and N. Di Lalla and R. Diaz Hernandez and M. A. DuVernois and J. C. Díaz-Vélez and K. Engel and T. Ergin and C. Espinoza and K. L. Fan and N. Fraija and S. Fraija and J. A. García-González and F. Garfias and N. Ghosh and A. Gonzalez Muñoz and M. M. González and J. A. González and J. A. Goodman and S. Groetsch and J. P. Harding and S. Hernández-Cadena and I. Herzog and D. Huang and P. Hüntemeyer and A. Iriarte and S. Kaufmann and D. Kieda and K. Leavitt and J. Lee and H. León Vargas and J. T. Linnemann and A. L. Longinotti and G. Luis-Raya and K. Malone and O. Martinez and J. Martínez-Castro and J. A. Matthews and P. Miranda-Romagnoli and P. E. Mirón-Enriquez and J. A. and Montes and J. A. Morales-Soto and E. Moreno and M. Najafi and A. and Nayerhoda and L. Nellen and N. Omodei and M. and Osorio and E. Ponce and Y. Pérez Araujo and E. G. Pérez-Pérez and C. D. Rho and A. Rodriguez Parra and D. Rosa-González and M. Roth and H. Salazar and A. Sandoval and J. Serna-Franco and A. J. Smith and Y. Son and R. W. Springer and O. Tibolla and K. Tollefson and I. Torres and R. Torres-Escobedo and E. Varela and L. Villaseñor and X. Wang and Z. Wang and I. J. Watson and H. Wu and S. Yu and H. Zhou and C. de León},
journal= {arXiv preprint arXiv:2506.18277},
year = {2025}
}