{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V3JLAGRPA3IVKR6FIXNVNSARV2","short_pith_number":"pith:V3JLAGRP","schema_version":"1.0","canonical_sha256":"aed2b01a2f06d15547c545db56c811ae82f03f2e092ad72994a4be9dec255eca","source":{"kind":"arxiv","id":"2509.06924","version":3},"attestation_state":"computed","paper":{"title":"Towards real-time surrogate-free Bayesian inversion for neutron reflectometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Andrew J. Parnell, Max D. Champneys, Maximilian W. A. Skoda, Patrick A. Fairclough, Philipp Gutfreund, Stephanie L. Burg, Timothy J. Rogers","submitted_at":"2025-09-08T17:38:01Z","abstract_excerpt":"Neutron reflectometry (NR) is a key enabling technology for many areas of scientific development. Although the forward reflectivity model is well-known, inferring the physical properties of a sample from NR data requires the solution of an inverse problem. Increasingly, beamline scientists are using NR in fast kinetic configurations and probing highly-complex structures and interfaces, introducing significant uncertainty. Existing uncertainty quantification (UQ) approaches in NR, such as Markov-Chain Monte-Carlo (MCMC), suffer from poor sample efficiency and slow convergence times. Recently, s"},"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":"2509.06924","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T17:38:01Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"2759989fe33a1e2bba33a6ecf53587d1274627514646a5f24e405aee31751d0b","abstract_canon_sha256":"68f6f37e9043ec8def9a3c9b08ffb0aac598b7a771f95afdb6ff22056e18ccdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:28.971433Z","signature_b64":"ZK5mnjU30IOAb2Exq5t2VRcekStUKYoO4YYe7N4T3je67M2FlomCqAtTaoURqeGj2ApWunc6aiieGPdw2o+NDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aed2b01a2f06d15547c545db56c811ae82f03f2e092ad72994a4be9dec255eca","last_reissued_at":"2026-07-29T01:25:28.970505Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:28.970505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards real-time surrogate-free Bayesian inversion for neutron reflectometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Andrew J. Parnell, Max D. Champneys, Maximilian W. A. Skoda, Patrick A. Fairclough, Philipp Gutfreund, Stephanie L. Burg, Timothy J. Rogers","submitted_at":"2025-09-08T17:38:01Z","abstract_excerpt":"Neutron reflectometry (NR) is a key enabling technology for many areas of scientific development. Although the forward reflectivity model is well-known, inferring the physical properties of a sample from NR data requires the solution of an inverse problem. Increasingly, beamline scientists are using NR in fast kinetic configurations and probing highly-complex structures and interfaces, introducing significant uncertainty. Existing uncertainty quantification (UQ) approaches in NR, such as Markov-Chain Monte-Carlo (MCMC), suffer from poor sample efficiency and slow convergence times. Recently, s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06924","kind":"arxiv","version":3},"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/2509.06924/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":"2509.06924","created_at":"2026-07-29T01:25:28.970946+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06924v3","created_at":"2026-07-29T01:25:28.970946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06924","created_at":"2026-07-29T01:25:28.970946+00:00"},{"alias_kind":"pith_short_12","alias_value":"V3JLAGRPA3IV","created_at":"2026-07-29T01:25:28.970946+00:00"},{"alias_kind":"pith_short_16","alias_value":"V3JLAGRPA3IVKR6F","created_at":"2026-07-29T01:25:28.970946+00:00"},{"alias_kind":"pith_short_8","alias_value":"V3JLAGRP","created_at":"2026-07-29T01:25:28.970946+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/V3JLAGRPA3IVKR6FIXNVNSARV2","json":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2.json","graph_json":"https://pith.science/api/pith-number/V3JLAGRPA3IVKR6FIXNVNSARV2/graph.json","events_json":"https://pith.science/api/pith-number/V3JLAGRPA3IVKR6FIXNVNSARV2/events.json","paper":"https://pith.science/paper/V3JLAGRP"},"agent_actions":{"view_html":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2","download_json":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2.json","view_paper":"https://pith.science/paper/V3JLAGRP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06924&json=true","fetch_graph":"https://pith.science/api/pith-number/V3JLAGRPA3IVKR6FIXNVNSARV2/graph.json","fetch_events":"https://pith.science/api/pith-number/V3JLAGRPA3IVKR6FIXNVNSARV2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2/action/storage_attestation","attest_author":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2/action/author_attestation","sign_citation":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2/action/citation_signature","submit_replication":"https://pith.science/pith/V3JLAGRPA3IVKR6FIXNVNSARV2/action/replication_record"}},"created_at":"2026-07-29T01:25:28.970946+00:00","updated_at":"2026-07-29T01:25:28.970946+00:00"}