{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6PYEOPJ5GDBH43J2JVIP5CGVQV","short_pith_number":"pith:6PYEOPJ5","schema_version":"1.0","canonical_sha256":"f3f0473d3d30c27e6d3a4d50fe88d5854640548a17c4135027663bd67ae7c9ef","source":{"kind":"arxiv","id":"1709.08610","version":2},"attestation_state":"computed","paper":{"title":"Numerical optimization for Artificial Retina Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"cs.CV","authors_text":"Andrey Ustyuzhanin, Denis Derkach, Maxim Borisyak, Mikhail Belous","submitted_at":"2017-09-25T17:33:11Z","abstract_excerpt":"High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples).\n  This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduc"},"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":"1709.08610","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-25T17:33:11Z","cross_cats_sorted":["hep-ex","physics.data-an"],"title_canon_sha256":"62efacf42eadd38875e01e3a75bb8aaa1ee8a25e01be4125a6932b508ade0de0","abstract_canon_sha256":"1e3948395a1a82e3dfb849eceb721080eed1231cd982ad94e3121e76dda77698"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:52.632503Z","signature_b64":"JeQmq0q6AMdTd+PP7SXvcaXtFozmxjlDydA/pEaKCGm+VipYwkBdTpzjne3eJjZBp6BGTfiiBY5U0srPc0O0CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3f0473d3d30c27e6d3a4d50fe88d5854640548a17c4135027663bd67ae7c9ef","last_reissued_at":"2026-05-18T00:28:52.632066Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:52.632066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Numerical optimization for Artificial Retina Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"cs.CV","authors_text":"Andrey Ustyuzhanin, Denis Derkach, Maxim Borisyak, Mikhail Belous","submitted_at":"2017-09-25T17:33:11Z","abstract_excerpt":"High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples).\n  This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08610","kind":"arxiv","version":2},"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":"1709.08610","created_at":"2026-05-18T00:28:52.632135+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08610v2","created_at":"2026-05-18T00:28:52.632135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08610","created_at":"2026-05-18T00:28:52.632135+00:00"},{"alias_kind":"pith_short_12","alias_value":"6PYEOPJ5GDBH","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6PYEOPJ5GDBH43J2","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6PYEOPJ5","created_at":"2026-05-18T12:31:03.183658+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/6PYEOPJ5GDBH43J2JVIP5CGVQV","json":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV.json","graph_json":"https://pith.science/api/pith-number/6PYEOPJ5GDBH43J2JVIP5CGVQV/graph.json","events_json":"https://pith.science/api/pith-number/6PYEOPJ5GDBH43J2JVIP5CGVQV/events.json","paper":"https://pith.science/paper/6PYEOPJ5"},"agent_actions":{"view_html":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV","download_json":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV.json","view_paper":"https://pith.science/paper/6PYEOPJ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08610&json=true","fetch_graph":"https://pith.science/api/pith-number/6PYEOPJ5GDBH43J2JVIP5CGVQV/graph.json","fetch_events":"https://pith.science/api/pith-number/6PYEOPJ5GDBH43J2JVIP5CGVQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV/action/storage_attestation","attest_author":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV/action/author_attestation","sign_citation":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV/action/citation_signature","submit_replication":"https://pith.science/pith/6PYEOPJ5GDBH43J2JVIP5CGVQV/action/replication_record"}},"created_at":"2026-05-18T00:28:52.632135+00:00","updated_at":"2026-05-18T00:28:52.632135+00:00"}