{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UOUYZDMWH7VMPZUTDAZOCAOUVO","short_pith_number":"pith:UOUYZDMW","schema_version":"1.0","canonical_sha256":"a3a98c8d963feac7e6931832e101d4abbdfc85359a3fd8da7db28d915ff5e463","source":{"kind":"arxiv","id":"2607.25282","version":1},"attestation_state":"computed","paper":{"title":"Normalizing Flows to Reconstruct Pseudo-PDFs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ph"],"primary_cat":"hep-lat","authors_text":"Kostas Orginos, Yamil Cahuana Medrano","submitted_at":"2026-07-28T04:40:25Z","abstract_excerpt":"We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework combines Gaussian Process priors with invertible neural networks to learn a posterior distribution over PDFs consistent with limited Ioffe-time data. We demonstrate that the architecture preserves physical constraints and extrapolation properties."},"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":"2607.25282","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-lat","submitted_at":"2026-07-28T04:40:25Z","cross_cats_sorted":["cs.LG","hep-ph"],"title_canon_sha256":"de1a455127213fcdb98a9fe7fca24254ca2bfdad4220f1e6b9a74824a115372c","abstract_canon_sha256":"ff7064bea6809364a59e0a263ad55c088fa1c5c7d718449645025e0eb1deb2ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:04.920519Z","signature_b64":"c0OVb6PPdrqUcj2j0GHr69KcKcIF4vriMFmyJSuqIns0gqfRK0jd0x0lJ82u3fZkRhRfWCPX/WYoFuPoBUO3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3a98c8d963feac7e6931832e101d4abbdfc85359a3fd8da7db28d915ff5e463","last_reissued_at":"2026-07-29T01:25:04.919563Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:04.919563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Normalizing Flows to Reconstruct Pseudo-PDFs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","hep-ph"],"primary_cat":"hep-lat","authors_text":"Kostas Orginos, Yamil Cahuana Medrano","submitted_at":"2026-07-28T04:40:25Z","abstract_excerpt":"We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework combines Gaussian Process priors with invertible neural networks to learn a posterior distribution over PDFs consistent with limited Ioffe-time data. We demonstrate that the architecture preserves physical constraints and extrapolation properties."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25282","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/2607.25282/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":"2607.25282","created_at":"2026-07-29T01:25:04.920015+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25282v1","created_at":"2026-07-29T01:25:04.920015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25282","created_at":"2026-07-29T01:25:04.920015+00:00"},{"alias_kind":"pith_short_12","alias_value":"UOUYZDMWH7VM","created_at":"2026-07-29T01:25:04.920015+00:00"},{"alias_kind":"pith_short_16","alias_value":"UOUYZDMWH7VMPZUT","created_at":"2026-07-29T01:25:04.920015+00:00"},{"alias_kind":"pith_short_8","alias_value":"UOUYZDMW","created_at":"2026-07-29T01:25:04.920015+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/UOUYZDMWH7VMPZUTDAZOCAOUVO","json":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO.json","graph_json":"https://pith.science/api/pith-number/UOUYZDMWH7VMPZUTDAZOCAOUVO/graph.json","events_json":"https://pith.science/api/pith-number/UOUYZDMWH7VMPZUTDAZOCAOUVO/events.json","paper":"https://pith.science/paper/UOUYZDMW"},"agent_actions":{"view_html":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO","download_json":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO.json","view_paper":"https://pith.science/paper/UOUYZDMW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25282&json=true","fetch_graph":"https://pith.science/api/pith-number/UOUYZDMWH7VMPZUTDAZOCAOUVO/graph.json","fetch_events":"https://pith.science/api/pith-number/UOUYZDMWH7VMPZUTDAZOCAOUVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO/action/storage_attestation","attest_author":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO/action/author_attestation","sign_citation":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO/action/citation_signature","submit_replication":"https://pith.science/pith/UOUYZDMWH7VMPZUTDAZOCAOUVO/action/replication_record"}},"created_at":"2026-07-29T01:25:04.920015+00:00","updated_at":"2026-07-29T01:25:04.920015+00:00"}