{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:N3BVNXIWCLNQP44TYLXX7EOPAR","short_pith_number":"pith:N3BVNXIW","schema_version":"1.0","canonical_sha256":"6ec356dd1612db07f393c2ef7f91cf044d1c11e8c5d9aea4a635d70bfaacb93e","source":{"kind":"arxiv","id":"2407.12119","version":5},"attestation_state":"computed","paper":{"title":"Graph Neural Network-Based Track Finding in the LHCb Vertex Detector","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Anthony Correia, Bertrand Granado, Fotis I. Giasemis, Nabil Garroum, Vladimir Vava Gligorov","submitted_at":"2024-07-16T19:05:38Z","abstract_excerpt":"The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a critical part of offline data processing pipelines. The complexity of HL-LHC data will however increasingly mandate track finding in all stages of an experiment's real-time processing. This paper presents a GNN-based track-finding pipeline tailored for the Run 3 LHCb experiment's vertex detector and benchmarks its physics performance and computational cost against "},"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":"2407.12119","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ins-det","submitted_at":"2024-07-16T19:05:38Z","cross_cats_sorted":["hep-ex","physics.data-an"],"title_canon_sha256":"e5bd536de6dc42d1d5a2501ee8de87d1b7da9b54f81ebee32ccc4b7e852a0d80","abstract_canon_sha256":"53d2759905eb6b13e31fe275e397001140caa36ed3651be5ffecd6931f891f57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:23.410383Z","signature_b64":"rZokMTKIO+L8E7Q98yXpzP4r2/iflUHtnqiJEY37p0+ENnWI7OIxcfAd82DhT/KYisel46qTyD8Mr+DKtjR5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ec356dd1612db07f393c2ef7f91cf044d1c11e8c5d9aea4a635d70bfaacb93e","last_reissued_at":"2026-07-05T11:26:23.409883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:23.409883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Network-Based Track Finding in the LHCb Vertex Detector","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"physics.ins-det","authors_text":"Anthony Correia, Bertrand Granado, Fotis I. Giasemis, Nabil Garroum, Vladimir Vava Gligorov","submitted_at":"2024-07-16T19:05:38Z","abstract_excerpt":"The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a critical part of offline data processing pipelines. The complexity of HL-LHC data will however increasingly mandate track finding in all stages of an experiment's real-time processing. This paper presents a GNN-based track-finding pipeline tailored for the Run 3 LHCb experiment's vertex detector and benchmarks its physics performance and computational cost against "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12119","kind":"arxiv","version":5},"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/2407.12119/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":"2407.12119","created_at":"2026-07-05T11:26:23.409943+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.12119v5","created_at":"2026-07-05T11:26:23.409943+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12119","created_at":"2026-07-05T11:26:23.409943+00:00"},{"alias_kind":"pith_short_12","alias_value":"N3BVNXIWCLNQ","created_at":"2026-07-05T11:26:23.409943+00:00"},{"alias_kind":"pith_short_16","alias_value":"N3BVNXIWCLNQP44T","created_at":"2026-07-05T11:26:23.409943+00:00"},{"alias_kind":"pith_short_8","alias_value":"N3BVNXIW","created_at":"2026-07-05T11:26:23.409943+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR","json":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR.json","graph_json":"https://pith.science/api/pith-number/N3BVNXIWCLNQP44TYLXX7EOPAR/graph.json","events_json":"https://pith.science/api/pith-number/N3BVNXIWCLNQP44TYLXX7EOPAR/events.json","paper":"https://pith.science/paper/N3BVNXIW"},"agent_actions":{"view_html":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR","download_json":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR.json","view_paper":"https://pith.science/paper/N3BVNXIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.12119&json=true","fetch_graph":"https://pith.science/api/pith-number/N3BVNXIWCLNQP44TYLXX7EOPAR/graph.json","fetch_events":"https://pith.science/api/pith-number/N3BVNXIWCLNQP44TYLXX7EOPAR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR/action/storage_attestation","attest_author":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR/action/author_attestation","sign_citation":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR/action/citation_signature","submit_replication":"https://pith.science/pith/N3BVNXIWCLNQP44TYLXX7EOPAR/action/replication_record"}},"created_at":"2026-07-05T11:26:23.409943+00:00","updated_at":"2026-07-05T11:26:23.409943+00:00"}