Abstract
Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
Recommended Citation
T. Capistrán and K. L. Fan and J. T. Linnemann and J. T. Linnemann and I. Torres and P. M. Saz Parkinson and P. L. Yu and A. U. Abeysekara and A. Albert and R. Alfaro and C. Alvarez and J. D. Álvarez and J. R. Angeles Camacho and J. C. Arteaga-Velázquez and K. P. Arunbabu and D. Avila Rojas and H. A. Ayala Solares and R. Babu and V. Baghmanyan and A. S. Barber and J. Becerra Gonzalez and E. Belmont-Moreno and S. Y. BenZvi and D. Berley and C. Brisbois and K. S. Caballero-Mora and T. Capistran and A. Carraminana and S. Casanova and O. Chaparro-Amaro and U. Cotti and J. Cotzomi and S. Coutino de Leon and E. De la Fuente and C. de Leon and L. Diaz-Cruz and R. Diaz Hernandez and J. C. Diaz-Velez and B. L. Dingus and M. Durocher and M. A. DuVernois and R. W. Ellsworth and K. Engel and C. Espinoza and K. L. Fan and K. Fang and M. Fernandez Alonso and B. Fick and H. Fleischhack and J. L. Flores and N. I. Fraija and D. Garcia and J. A. Garcia-Gonzalez and J. L. Garcia-Luna and G. Garcia-Torales and F. Garfias and H. Goksu and M. M. Gonzalez and J. A. Goodman and J. P. Harding and S. Hernandez and I. Herzog and J. Hinton and B. Hona and D. Huang and F. Hueyotl-Zahuantitla and C. M. Hui and B. Humensky and P. Huntemeyer and A. Iriarte and A. Jardin-Blicq and H. Jhee and V. Joshi and D. Kieda and G. J. Kunde and S. Kunwar and A. Lara and J. Lee and W. H. Lee and D. Lennarz and H. Leon Vargas and J. Linnemann and A. L. Longinotti and R. Lopez-Coto and G. Luis-Raya and J. Lundeen and K. Malone and V. Marandon and O. Martinez and I. Martinez-Castellanos and H. Martinez-Huerta and J. Martinez-Castro and J. A. Matthews and J. McEnery and P. Miranda-Romagnoli and J. A. Morales-Soto and E. Moreno and M. Mostafa and A. Nayerhoda and L. Nellen and M. Newbold and M. U. Nisa and R. Noriega-Papaqui and L. Olivera-Nieto and N. Omodei and A. Peisker and Y. Perez Araujo and E. G. Perez-Perez and C. D. Rho and C. Riviere and D. Rosa-Gonzalez and E. Ruiz-Velasco and J. Ryan and H. Salazar and F. Salesa Greus and A. Sandoval and M. Schneider and H. Schoorlemmer and J. Serna-Franco and G. Sinnis and A. J. Smith and R. W. Springer and P. Surajbali and I. Taboada and M. Tanner and K. Tollefson and I. Torres and R. Torres-Escobedo and R. Turner and F. Urena-Mena and L. Villasenor and X. Wang and I. J. Watson and T. Weisgarber and F. Werner and E. Willox and J. Wood and G. B. Yodh and A. Zepeda and H. Zhou, "Use Of Machine Learning For Gamma/hadron Separation With HAWC," Proceedings of Science, vol. 395, article no. 745, Sissa Medialab Srl, Mar 2022.
The definitive version is available at https://doi.org/10.48550/arXiv.2108.00112
Department(s)
Physics
International Standard Serial Number (ISSN)
1824-8039
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Sissa Medialab Srl, All rights reserved.
Publication Date
18 Mar 2022
Comments
National Science Foundation, Grant PRODEP-SEP UDG-CA-499