{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:IAXRP7KO36QRVE2ST2LYD5I6U3","short_pith_number":"pith:IAXRP7KO","schema_version":"1.0","canonical_sha256":"402f17fd4edfa11a93529e9781f51ea6d324d70b9bf5928ec67496dc3addcc4b","source":{"kind":"arxiv","id":"1408.0150","version":3},"attestation_state":"computed","paper":{"title":"The Proton Radius from Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nucl-ex","nucl-th"],"primary_cat":"hep-ph","authors_text":"Cezary Juszczak, Krzysztof M. Graczyk","submitted_at":"2014-08-01T12:23:44Z","abstract_excerpt":"The methods of Bayesian statistics are used to extract the value of the proton radius from the elastic $ep$ scattering data in a model independent way. To achieve that goal a large number of parametrizations (equivalent to neural network schemes) are considered and ranked by their conditional probability $P(\\mathrm{parametrization}\\,|\\,\\mathrm{data})$ instead of using the minimal error criterion. As a result the most probable proton radii values ($r_E^p=0.899\\pm 0.003$ fm, $r_M^p=0.879\\pm 0.007$ fm) are obtained and systematic error due to freedom in the choice of parametrization is estimated."},"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":"1408.0150","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2014-08-01T12:23:44Z","cross_cats_sorted":["nucl-ex","nucl-th"],"title_canon_sha256":"14e7d6ed8d127b172d0289e6229201f14aa009d051487b634d394e3a4bfefe53","abstract_canon_sha256":"8a06d1ee46c1b1925be14cc01bda3106a11e603801f35e3fde36009fe2062bc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:09:54.572847Z","signature_b64":"2+yezSDqv5u0BW3dGIW+hNhWPHWbqr0OM3nxVlBEkHa0asAQCNtaI4yyABr2vHJG/RZy27q4npTHhX5XY1EFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"402f17fd4edfa11a93529e9781f51ea6d324d70b9bf5928ec67496dc3addcc4b","last_reissued_at":"2026-05-18T02:09:54.572133Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:09:54.572133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Proton Radius from Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nucl-ex","nucl-th"],"primary_cat":"hep-ph","authors_text":"Cezary Juszczak, Krzysztof M. Graczyk","submitted_at":"2014-08-01T12:23:44Z","abstract_excerpt":"The methods of Bayesian statistics are used to extract the value of the proton radius from the elastic $ep$ scattering data in a model independent way. To achieve that goal a large number of parametrizations (equivalent to neural network schemes) are considered and ranked by their conditional probability $P(\\mathrm{parametrization}\\,|\\,\\mathrm{data})$ instead of using the minimal error criterion. As a result the most probable proton radii values ($r_E^p=0.899\\pm 0.003$ fm, $r_M^p=0.879\\pm 0.007$ fm) are obtained and systematic error due to freedom in the choice of parametrization is estimated."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.0150","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":""},"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":"1408.0150","created_at":"2026-05-18T02:09:54.572249+00:00"},{"alias_kind":"arxiv_version","alias_value":"1408.0150v3","created_at":"2026-05-18T02:09:54.572249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.0150","created_at":"2026-05-18T02:09:54.572249+00:00"},{"alias_kind":"pith_short_12","alias_value":"IAXRP7KO36QR","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"IAXRP7KO36QRVE2S","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"IAXRP7KO","created_at":"2026-05-18T12:28:33.132498+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2308.13222","citing_title":"Bayesian Reasoning for Physics Informed Neural Networks","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3","json":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3.json","graph_json":"https://pith.science/api/pith-number/IAXRP7KO36QRVE2ST2LYD5I6U3/graph.json","events_json":"https://pith.science/api/pith-number/IAXRP7KO36QRVE2ST2LYD5I6U3/events.json","paper":"https://pith.science/paper/IAXRP7KO"},"agent_actions":{"view_html":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3","download_json":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3.json","view_paper":"https://pith.science/paper/IAXRP7KO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1408.0150&json=true","fetch_graph":"https://pith.science/api/pith-number/IAXRP7KO36QRVE2ST2LYD5I6U3/graph.json","fetch_events":"https://pith.science/api/pith-number/IAXRP7KO36QRVE2ST2LYD5I6U3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3/action/storage_attestation","attest_author":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3/action/author_attestation","sign_citation":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3/action/citation_signature","submit_replication":"https://pith.science/pith/IAXRP7KO36QRVE2ST2LYD5I6U3/action/replication_record"}},"created_at":"2026-05-18T02:09:54.572249+00:00","updated_at":"2026-05-18T02:09:54.572249+00:00"}