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HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation

Fuente: arXiv

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Main 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.
Format:	Preprint
Published:	2025
Subjects:	Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena
Online Access:	Acceder al recurso
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author	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.
author_facet	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.
contents	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).
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_18277
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation 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. Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena 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).
title	HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
topic	Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena
url	https://arxiv.org/abs/2506.18277