{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZKL7YV6LBLMWGTSC5SS2UPM43U","short_pith_number":"pith:ZKL7YV6L","schema_version":"1.0","canonical_sha256":"ca97fc57cb0ad9634e42eca5aa3d9cdd08efa92dcaf09422cf4cc33934c60cca","source":{"kind":"arxiv","id":"2506.18277","version":1},"attestation_state":"computed","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 "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.18277","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-06-23T04:13:53Z","cross_cats_sorted":["astro-ph.HE"],"title_canon_sha256":"8dcfded004a7073e4e561fc2f1335f333d626086fcbd81b1a47e347f662fb2ab","abstract_canon_sha256":"3c4dd5ad12236a4eb72821215a46a153a5a35a5e39a31bdfcf7cd6aeac370efa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:47.825064Z","signature_b64":"2d5RJxOFhAdrjAT2swQZWJMLUYQzF5cDUk/QLzmETdyjU2UBKAS/amiGN25PvMiaEPQgFc81eIVMXvMkYCIyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca97fc57cb0ad9634e42eca5aa3d9cdd08efa92dcaf09422cf4cc33934c60cca","last_reissued_at":"2026-07-05T11:25:47.824466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:47.824466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.18277","created_at":"2026-07-05T11:25:47.824546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.18277v1","created_at":"2026-07-05T11:25:47.824546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18277","created_at":"2026-07-05T11:25:47.824546+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKL7YV6LBLMW","created_at":"2026-07-05T11:25:47.824546+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKL7YV6LBLMWGTSC","created_at":"2026-07-05T11:25:47.824546+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKL7YV6L","created_at":"2026-07-05T11:25:47.824546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10828","citing_title":"A Multiwavelength Interpretation of HESS J1857+026 Emission Using the Fermi-LAT, VERITAS, and HAWC Observatories","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2602.05955","citing_title":"Improved Heavy Dark Matter Annihilation Search from Dwarf Galaxies with HAWC","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U","json":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U.json","graph_json":"https://pith.science/api/pith-number/ZKL7YV6LBLMWGTSC5SS2UPM43U/graph.json","events_json":"https://pith.science/api/pith-number/ZKL7YV6LBLMWGTSC5SS2UPM43U/events.json","paper":"https://pith.science/paper/ZKL7YV6L"},"agent_actions":{"view_html":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U","download_json":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U.json","view_paper":"https://pith.science/paper/ZKL7YV6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.18277&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKL7YV6LBLMWGTSC5SS2UPM43U/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKL7YV6LBLMWGTSC5SS2UPM43U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U/action/storage_attestation","attest_author":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U/action/author_attestation","sign_citation":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U/action/citation_signature","submit_replication":"https://pith.science/pith/ZKL7YV6LBLMWGTSC5SS2UPM43U/action/replication_record"}},"created_at":"2026-07-05T11:25:47.824546+00:00","updated_at":"2026-07-05T11:25:47.824546+00:00"}