{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DIMEKSZLM7RP4Q7BEQZZF4HAD6","short_pith_number":"pith:DIMEKSZL","schema_version":"1.0","canonical_sha256":"1a18454b2b67e2fe43e1243392f0e01fb1e26217ce6183499c5274bc8caf1615","source":{"kind":"arxiv","id":"2107.11012","version":1},"attestation_state":"computed","paper":{"title":"Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"nucl-th","authors_text":"Fupeng Li, Hongliang L\\\"u, Kai Zhou, Qingfeng Li, Yongjia Wang","submitted_at":"2021-07-23T03:23:10Z","abstract_excerpt":"A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\\rm sym}(\\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labelled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff $E_{\\rm sym}(\\rho)$ is about 60% due to large event-by-eve"},"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":"2107.11012","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"nucl-th","submitted_at":"2021-07-23T03:23:10Z","cross_cats_sorted":["nucl-ex"],"title_canon_sha256":"5100180832ba450bf8e1360bb83cffda0c9a0b9141da48acc3015339084baaf1","abstract_canon_sha256":"87a04e354da65106a3d63ab3104ecb67fa66acbc598b2121b49ea0a2621b5f78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:18:20.951996Z","signature_b64":"ceUPpzaHakJzRvM3CeS+YA/nhrH/CiWpTZ8fkVNZHLJ1zCjaT6KtKAonG7Qpp7A7LttsxnW+WSBkxGli3iLcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a18454b2b67e2fe43e1243392f0e01fb1e26217ce6183499c5274bc8caf1615","last_reissued_at":"2026-07-05T03:18:20.951533Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:18:20.951533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"nucl-th","authors_text":"Fupeng Li, Hongliang L\\\"u, Kai Zhou, Qingfeng Li, Yongjia Wang","submitted_at":"2021-07-23T03:23:10Z","abstract_excerpt":"A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\\rm sym}(\\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labelled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff $E_{\\rm sym}(\\rho)$ is about 60% due to large event-by-eve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11012","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/2107.11012/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":"2107.11012","created_at":"2026-07-05T03:18:20.951591+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.11012v1","created_at":"2026-07-05T03:18:20.951591+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11012","created_at":"2026-07-05T03:18:20.951591+00:00"},{"alias_kind":"pith_short_12","alias_value":"DIMEKSZLM7RP","created_at":"2026-07-05T03:18:20.951591+00:00"},{"alias_kind":"pith_short_16","alias_value":"DIMEKSZLM7RP4Q7B","created_at":"2026-07-05T03:18:20.951591+00:00"},{"alias_kind":"pith_short_8","alias_value":"DIMEKSZL","created_at":"2026-07-05T03:18:20.951591+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/DIMEKSZLM7RP4Q7BEQZZF4HAD6","json":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6.json","graph_json":"https://pith.science/api/pith-number/DIMEKSZLM7RP4Q7BEQZZF4HAD6/graph.json","events_json":"https://pith.science/api/pith-number/DIMEKSZLM7RP4Q7BEQZZF4HAD6/events.json","paper":"https://pith.science/paper/DIMEKSZL"},"agent_actions":{"view_html":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6","download_json":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6.json","view_paper":"https://pith.science/paper/DIMEKSZL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.11012&json=true","fetch_graph":"https://pith.science/api/pith-number/DIMEKSZLM7RP4Q7BEQZZF4HAD6/graph.json","fetch_events":"https://pith.science/api/pith-number/DIMEKSZLM7RP4Q7BEQZZF4HAD6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6/action/storage_attestation","attest_author":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6/action/author_attestation","sign_citation":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6/action/citation_signature","submit_replication":"https://pith.science/pith/DIMEKSZLM7RP4Q7BEQZZF4HAD6/action/replication_record"}},"created_at":"2026-07-05T03:18:20.951591+00:00","updated_at":"2026-07-05T03:18:20.951591+00:00"}