{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZEX6WC23QOKVOEVFL2ZV22AVI2","short_pith_number":"pith:ZEX6WC23","schema_version":"1.0","canonical_sha256":"c92feb0b5b83955712a55eb35d681546a613111ca62b94691747795eb2d7f723","source":{"kind":"arxiv","id":"2408.03784","version":2},"attestation_state":"computed","paper":{"title":"Neural Network Modeling of Heavy-Quark Potential from Holography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Fu-Peng Li, Kai Zhou, Ou-Yang Luo, Xiao-Hua Li, Xun Chen","submitted_at":"2024-08-07T14:09:25Z","abstract_excerpt":"Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, ma"},"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":"2408.03784","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2024-08-07T14:09:25Z","cross_cats_sorted":[],"title_canon_sha256":"86815fee0848c4d732e1f8dd1da072968f09704b911b98c70c70ac9153248a75","abstract_canon_sha256":"79d3e4dfb9d84e0290c75014956f5ee96ffeab233a05235b222796a38b2532b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:32.246970Z","signature_b64":"kLgFzyYfuEoUrZ4NDLP6XkNiZIGJu+xZj4QmegHpFniP6ctu6c9iDuL7GJpg359GuPJD4Rgir8uBddD7RB66CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c92feb0b5b83955712a55eb35d681546a613111ca62b94691747795eb2d7f723","last_reissued_at":"2026-07-05T09:43:32.246493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:32.246493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network Modeling of Heavy-Quark Potential from Holography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Fu-Peng Li, Kai Zhou, Ou-Yang Luo, Xiao-Hua Li, Xun Chen","submitted_at":"2024-08-07T14:09:25Z","abstract_excerpt":"Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03784","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/2408.03784/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":"2408.03784","created_at":"2026-07-05T09:43:32.246555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03784v2","created_at":"2026-07-05T09:43:32.246555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03784","created_at":"2026-07-05T09:43:32.246555+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEX6WC23QOKV","created_at":"2026-07-05T09:43:32.246555+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEX6WC23QOKVOEVF","created_at":"2026-07-05T09:43:32.246555+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEX6WC23","created_at":"2026-07-05T09:43:32.246555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02906","citing_title":"Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14638","citing_title":"Probing bulk geometry via pole skipping: from static to rotating spacetimes","ref_index":101,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2","json":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2.json","graph_json":"https://pith.science/api/pith-number/ZEX6WC23QOKVOEVFL2ZV22AVI2/graph.json","events_json":"https://pith.science/api/pith-number/ZEX6WC23QOKVOEVFL2ZV22AVI2/events.json","paper":"https://pith.science/paper/ZEX6WC23"},"agent_actions":{"view_html":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2","download_json":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2.json","view_paper":"https://pith.science/paper/ZEX6WC23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03784&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEX6WC23QOKVOEVFL2ZV22AVI2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEX6WC23QOKVOEVFL2ZV22AVI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2/action/storage_attestation","attest_author":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2/action/author_attestation","sign_citation":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2/action/citation_signature","submit_replication":"https://pith.science/pith/ZEX6WC23QOKVOEVFL2ZV22AVI2/action/replication_record"}},"created_at":"2026-07-05T09:43:32.246555+00:00","updated_at":"2026-07-05T09:43:32.246555+00:00"}