{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ZHDFVW7HN2U4ZFA2AJJILYARRB","short_pith_number":"pith:ZHDFVW7H","schema_version":"1.0","canonical_sha256":"c9c65adbe76ea9cc941a025285e01188420a31cd5feb1f601fb2c4327b5e4c2a","source":{"kind":"arxiv","id":"1604.01444","version":3},"attestation_state":"computed","paper":{"title":"A Convolutional Neural Network Neutrino Event Classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"hep-ex","authors_text":"A. Aurisano, A. Himmel, A. Radovic, A. Sousa, D. Rocco, E. Niner, F. Psihas, G. Pawloski, M. D. Messier, P. Vahle","submitted_at":"2016-04-05T22:41:13Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutiona"},"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":"1604.01444","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ex","submitted_at":"2016-04-05T22:41:13Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"db3e546e4b4c70d296585b18276d0cc931f22a4dd90b8a695c8db27a102696f3","abstract_canon_sha256":"685992d45826663fa41da204d7c16bc41d0a0145c682a923259019eda95423a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:05:43.557371Z","signature_b64":"PNFhpT1sXcsqroM1FDLC2dTlSeu/0Y89diiinsbEms3fZUTyJP0HWlfr+lIBB6HZNdiQi3E31JQGCxgLP4YICA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9c65adbe76ea9cc941a025285e01188420a31cd5feb1f601fb2c4327b5e4c2a","last_reissued_at":"2026-05-18T01:05:43.556931Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:05:43.556931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Convolutional Neural Network Neutrino Event Classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"hep-ex","authors_text":"A. Aurisano, A. Himmel, A. Radovic, A. Sousa, D. Rocco, E. Niner, F. Psihas, G. Pawloski, M. D. Messier, P. Vahle","submitted_at":"2016-04-05T22:41:13Z","abstract_excerpt":"Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutiona"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.01444","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1604.01444","created_at":"2026-05-18T01:05:43.556997+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.01444v3","created_at":"2026-05-18T01:05:43.556997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.01444","created_at":"2026-05-18T01:05:43.556997+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHDFVW7HN2U4","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHDFVW7HN2U4ZFA2","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHDFVW7H","created_at":"2026-05-18T12:30:53.716459+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.18861","citing_title":"Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2604.21869","citing_title":"Analytical and Machine Learning Methods for Model Discernment at CE$\\nu$NS Experiments","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07037","citing_title":"Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB","json":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB.json","graph_json":"https://pith.science/api/pith-number/ZHDFVW7HN2U4ZFA2AJJILYARRB/graph.json","events_json":"https://pith.science/api/pith-number/ZHDFVW7HN2U4ZFA2AJJILYARRB/events.json","paper":"https://pith.science/paper/ZHDFVW7H"},"agent_actions":{"view_html":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB","download_json":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB.json","view_paper":"https://pith.science/paper/ZHDFVW7H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.01444&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHDFVW7HN2U4ZFA2AJJILYARRB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHDFVW7HN2U4ZFA2AJJILYARRB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB/action/storage_attestation","attest_author":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB/action/author_attestation","sign_citation":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB/action/citation_signature","submit_replication":"https://pith.science/pith/ZHDFVW7HN2U4ZFA2AJJILYARRB/action/replication_record"}},"created_at":"2026-05-18T01:05:43.556997+00:00","updated_at":"2026-05-18T01:05:43.556997+00:00"}