{"paper":{"title":"HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE"],"primary_cat":"astro-ph.IM","authors_text":"A., A. Andr\\'es, A. Bernal, A. Carrami\\~nana, A. Gonzalez Mu\\~noz, A. Iriarte, A.J. Smith, A.L. Longinotti, A. Rodriguez Parra, A. Sandoval, C. Alvarez, C. de Le\\'on, C.D. Rho, C. Espinoza, D. Avila Rojas, D. Depaoli, D. Huang, D. Kieda, D. Rosa-Gonz\\'alez, E. Anita-Rangel, E. Belmont-Moreno, E. De la Fuente, E.G. P\\'erez-P\\'erez, E. Moreno, E. Ponce, E. Varela, F. Carre\\'on, F. Garfias, G. Luis-Raya, H.A. Ayala Solares, H. Le\\'on Vargas, H. Salazar, H. Wu, H. Zhou, I. Herzog, I.J. Watson, I. Torres, J.A., J.A. Garc\\'ia-Gonz\\'alez, J.A. Gonz\\'alez, J.A. Goodman, J.A. Matthews, J.A. Morales-Soto, J.C. Arteaga-Vel\\'azquez, J.C. D\\'iaz-V\\'elez, J. Lee, J. Mart\\'inez-Castro, J.P. Harding, J. Serna-Franco, J.T. Linnemann, K. Engel, K. Leavitt, K.L. Fan, K. Malone, K. Tollefson, L. Nellen, L. Villase\\~nor, M., M.A. DuVernois, M. Araya, M.M. Gonz\\'alez, M. Najafi, Montes, M. Roth, Nayerhoda, N. Di Lalla, N. Fraija, N. Ghosh, N. Omodei, O. Martinez, Osorio, O. Tibolla, P. Bangale, P. Desiati, P.E. Mir\\'on-Enriquez, P. H\\\"untemeyer, P. Miranda-Romagnoli, R. Alfaro, R. Babu, R. Diaz Hernandez, R. Torres-Escobedo, R.W. Springer, S. Casanova, S. Fraija, S. Groetsch, S. Hern\\'andez-Cadena, S. Kaufmann, S. Yu, T. Capistr\\'an, T. Ergin, U. Cotti, X. Wang, Y. P\\'erez Araujo, Y. Son, Z. Wang","submitted_at":"2025-06-23T04:13:53Z","abstract_excerpt":"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 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18277","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.18277/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}