{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:26YZJHXEXX24EFRKU5ECKBCIVX","short_pith_number":"pith:26YZJHXE","schema_version":"1.0","canonical_sha256":"d7b1949ee4bdf5c2162aa748250448adf30f69819d4a0856fa35fddea82c9bfd","source":{"kind":"arxiv","id":"2605.13000","version":1},"attestation_state":"computed","paper":{"title":"Neural Network Generalized Parton Distributions (NNGPD)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Neural networks can reconstruct generalized parton distributions by training on both experimental data and lattice QCD results.","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Simonetta Liuti, Zaki Panjsheeri","submitted_at":"2026-05-13T04:56:42Z","abstract_excerpt":"Generalized parton distributions (GPDs) serve as indispensable tools for the exploration of proton structure. In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD)."},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.13000","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2026-05-13T04:56:42Z","cross_cats_sorted":[],"title_canon_sha256":"6efa7df410e8dcfc0ad713e1840865456013468d43c9b679e997605c26a4fe68","abstract_canon_sha256":"75436de947cc073b9ecb514bc2a81d4f60c456bb677988b6dafea7246152bab8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:00.436243Z","signature_b64":"4Hk5vr+BVgQ/yaVQq7x4Y/MSNAeREdTg5Lt5KSvIXtScCD5Q715BTgCwXn4dKC25iLAiMrh+ZNvzqHpd3TlYDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7b1949ee4bdf5c2162aa748250448adf30f69819d4a0856fa35fddea82c9bfd","last_reissued_at":"2026-05-18T03:09:00.435772Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:00.435772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network Generalized Parton Distributions (NNGPD)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Neural networks can reconstruct generalized parton distributions by training on both experimental data and lattice QCD results.","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Simonetta Liuti, Zaki Panjsheeri","submitted_at":"2026-05-13T04:56:42Z","abstract_excerpt":"Generalized parton distributions (GPDs) serve as indispensable tools for the exploration of proton structure. In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD)."},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a neural network trained on available data and LQCD results can accurately and unbiasedly reconstruct the full GPD functions without overfitting or missing important physical constraints.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A deep learning-assisted framework extracts generalized parton distributions from experimental data and ab-initio lattice QCD results.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Neural networks can reconstruct generalized parton distributions by training on both experimental data and lattice QCD results.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1f8723969749cadb2b21c8b511b407da44d7887471f10783b607d7a99b546431"},"source":{"id":"2605.13000","kind":"arxiv","version":1},"verdict":{"id":"978e4257-de0e-4e0d-a220-1941826c2268","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:54:58.409310Z","strongest_claim":"In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD).","one_line_summary":"A deep learning-assisted framework extracts generalized parton distributions from experimental data and ab-initio lattice QCD results.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a neural network trained on available data and LQCD results can accurately and unbiasedly reconstruct the full GPD functions without overfitting or missing important physical constraints.","pith_extraction_headline":"Neural networks can reconstruct generalized parton distributions by training on both experimental data and lattice QCD results."},"references":{"count":15,"sample":[{"doi":"","year":2025,"title":"AI for nuclear physics: the EXCLAIM project.JINST, 20(08):C08011, 2025","work_id":"74bf365d-04ee-48ce-80c5-9728686437a0","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1997,"title":"Gauge-InvariantDecompositionofNucleonSpin.Phys","work_id":"356172a3-98f5-4289-ba32-8668c83ec563","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1997,"title":"A. V. Radyushkin. Nonforward parton distributions.Phys. Rev. D, 56:5524–5557, 1997","work_id":"728e85bb-5ab5-4f37-a43e-27852d649529","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1990,"title":"R. L. Jaffe and Aneesh Manohar. The𝑔1 Problem: Fact and Fantasy on the Spin of the Proton.Nucl. Phys. B, 337:509–546, 1990","work_id":"ffd06c78-751e-483d-8f25-aa6d964c1f11","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Generalized Parton Distributions from Symbolic Regression","work_id":"6bc583a1-c434-4ff0-b571-bb89c46daf84","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":15,"snapshot_sha256":"585b497493b7ef12e79e892ff9fd0502f31f2eae33e308542ebd018e8349c274","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":"2605.13000","created_at":"2026-05-18T03:09:00.435840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.13000v1","created_at":"2026-05-18T03:09:00.435840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13000","created_at":"2026-05-18T03:09:00.435840+00:00"},{"alias_kind":"pith_short_12","alias_value":"26YZJHXEXX24","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"26YZJHXEXX24EFRK","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"26YZJHXE","created_at":"2026-05-18T12:33:37.589309+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/26YZJHXEXX24EFRKU5ECKBCIVX","json":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX.json","graph_json":"https://pith.science/api/pith-number/26YZJHXEXX24EFRKU5ECKBCIVX/graph.json","events_json":"https://pith.science/api/pith-number/26YZJHXEXX24EFRKU5ECKBCIVX/events.json","paper":"https://pith.science/paper/26YZJHXE"},"agent_actions":{"view_html":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX","download_json":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX.json","view_paper":"https://pith.science/paper/26YZJHXE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.13000&json=true","fetch_graph":"https://pith.science/api/pith-number/26YZJHXEXX24EFRKU5ECKBCIVX/graph.json","fetch_events":"https://pith.science/api/pith-number/26YZJHXEXX24EFRKU5ECKBCIVX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX/action/storage_attestation","attest_author":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX/action/author_attestation","sign_citation":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX/action/citation_signature","submit_replication":"https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX/action/replication_record"}},"created_at":"2026-05-18T03:09:00.435840+00:00","updated_at":"2026-05-18T03:09:00.435840+00:00"}