HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Year: 2025
Type: preprint
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).
Source: arXiv (Cornell University)
Authors: Alfaro, R., Alvarez, C., Andrés, A., Anita-Rangel, E., Araya, M., Arteaga-Velázquez, J. C., Rojas, D. Avila, Solares, H. A. Ayala, Babu, R., Bangale, P., Belmont-Moreno, E., Bernal, A., Capistrán, T., Carramiñana, A., Carreón, F., Casanova, S., Cotti, U., De la Fuente, E., Depaoli, D., Desiati, P., Di Lalla, N., Hernandez, R. Diaz, DuVernois, M. A., Díaz-Vélez, J. C., Engel, K., Ergin, T., Espinoza, C., Fan, K. L., Fraija, N., Fraija, S., García-González, J. A., Garfias, F., Ghosh, N., Muñoz, A. Gonzalez, González, M. M., González, J. A., Goodman, J. A., Groetsch, S., Harding, J. P., Hernández-Cadena, S., Herzog, I., Huang, D., Hüntemeyer, P., Iriarte, A., Kaufmann, S., Kieda, D., Leavitt, K., Lee, J., Vargas, H. León, Linnemann, J. T., Longinotti, A. L., Luis-Raya, G., Malone, K., Martinez, O., Martínez-Castro, J., Matthews, J. A., Miranda-Romagnoli, P., Mirón-Enriquez, P. E., A., J., Montes, Morales-Soto, J. A., Moreno, E., Najafi, M., A., Nayerhoda, Nellen, L., Omodei, N., M., Osorio, Ponce, E., Araujo, Y. Pérez, Pérez-Pérez, E. G., Rho, C. D., Parra, A. Rodriguez, Rosa-González, D., Roth, M., Salazar, H., Sandoval, A., Serna-Franco, J., Smith, A. J., Son, Y., Springer, R. W., Tibolla, O., Tollefson, K., Torres, I., Torres-Escobedo, R., Varela, E., Villaseñor, L., Wang, X., Wang, Z., Watson, I. J., Wu, H., Yu, S., Zhou, H., de León, C.
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891870299
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