{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QFR632QL4ZNUQ53QVZPZOWZLJ4","short_pith_number":"pith:QFR632QL","schema_version":"1.0","canonical_sha256":"8163edea0be65b487770ae5f975b2b4f33a4373fac12ce9efd82958c1532fb9a","source":{"kind":"arxiv","id":"2112.03824","version":2},"attestation_state":"computed","paper":{"title":"Determination of impact parameter in high-energy heavy-ion collisions via deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Pei Xiang, Xu-Guang Huang, Yuan-Sheng Zhao","submitted_at":"2021-12-07T17:02:09Z","abstract_excerpt":"In this study, Au+Au collisions with the impact parameter of $0 \\leq b \\leq 12.5$ fm at $\\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about $0.4$ fm with CNN behaving sl"},"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":"2112.03824","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2021-12-07T17:02:09Z","cross_cats_sorted":[],"title_canon_sha256":"6748b3b55b6aeb1a938d7d4530249c01bfe72814e575326d67d1cb130026ec2d","abstract_canon_sha256":"f07a8a6c8613af3c06351bd4ee9e6cea69d044a58ed46d32710e215fbda3f8dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:39:24.522293Z","signature_b64":"Wn2p8Me5F2/OdUxYILZ6Vfax1Yv/pY9lBe4tLDe8Lwiu7aZIq6S0xKAkBAxVbiQsYvKlV5NZZihZmNufTmiiBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8163edea0be65b487770ae5f975b2b4f33a4373fac12ce9efd82958c1532fb9a","last_reissued_at":"2026-07-05T04:39:24.521857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:39:24.521857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Determination of impact parameter in high-energy heavy-ion collisions via deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Pei Xiang, Xu-Guang Huang, Yuan-Sheng Zhao","submitted_at":"2021-12-07T17:02:09Z","abstract_excerpt":"In this study, Au+Au collisions with the impact parameter of $0 \\leq b \\leq 12.5$ fm at $\\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about $0.4$ fm with CNN behaving sl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.03824","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.03824/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":"2112.03824","created_at":"2026-07-05T04:39:24.521916+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.03824v2","created_at":"2026-07-05T04:39:24.521916+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.03824","created_at":"2026-07-05T04:39:24.521916+00:00"},{"alias_kind":"pith_short_12","alias_value":"QFR632QL4ZNU","created_at":"2026-07-05T04:39:24.521916+00:00"},{"alias_kind":"pith_short_16","alias_value":"QFR632QL4ZNUQ53Q","created_at":"2026-07-05T04:39:24.521916+00:00"},{"alias_kind":"pith_short_8","alias_value":"QFR632QL","created_at":"2026-07-05T04:39:24.521916+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06897","citing_title":"Machine learning the impact parameter in heavy-ion collisions at $\\sqrt{s_{\\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM","ref_index":66,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4","json":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4.json","graph_json":"https://pith.science/api/pith-number/QFR632QL4ZNUQ53QVZPZOWZLJ4/graph.json","events_json":"https://pith.science/api/pith-number/QFR632QL4ZNUQ53QVZPZOWZLJ4/events.json","paper":"https://pith.science/paper/QFR632QL"},"agent_actions":{"view_html":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4","download_json":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4.json","view_paper":"https://pith.science/paper/QFR632QL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.03824&json=true","fetch_graph":"https://pith.science/api/pith-number/QFR632QL4ZNUQ53QVZPZOWZLJ4/graph.json","fetch_events":"https://pith.science/api/pith-number/QFR632QL4ZNUQ53QVZPZOWZLJ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4/action/storage_attestation","attest_author":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4/action/author_attestation","sign_citation":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4/action/citation_signature","submit_replication":"https://pith.science/pith/QFR632QL4ZNUQ53QVZPZOWZLJ4/action/replication_record"}},"created_at":"2026-07-05T04:39:24.521916+00:00","updated_at":"2026-07-05T04:39:24.521916+00:00"}