{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:GM2QMXV6VP5CD7XJR43LQ3IIJH","short_pith_number":"pith:GM2QMXV6","schema_version":"1.0","canonical_sha256":"3335065ebeabfa21fee98f36b86d0849f384d90b9936a3cc5be0bec798b62ea9","source":{"kind":"arxiv","id":"1508.06263","version":1},"attestation_state":"computed","paper":{"title":"Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.SR","nucl-ex"],"primary_cat":"nucl-th","authors_text":"H. B. Prosper, J. Piekarewicz, R. Utama","submitted_at":"2015-08-25T19:39:57Z","abstract_excerpt":"Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing \"state-of-the-art\" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are "},"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":"1508.06263","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"nucl-th","submitted_at":"2015-08-25T19:39:57Z","cross_cats_sorted":["astro-ph.HE","astro-ph.SR","nucl-ex"],"title_canon_sha256":"5016fe5b376c272d0380174be071dfe0ff9475c2305c7f8f9abe4b20f143db0a","abstract_canon_sha256":"d1a31db84c65e0153bdcffeb3091dc8a383017ff768b63f1fbb8c077ed7dfe3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:10.300897Z","signature_b64":"h98tZlDdf7vdSfgDbFS0uGtzkDEISRfDbKV+KL/ybTaeWCv5MpskV4C4dM2czZxfKdVVi8jwH62+9Vy3YtM0BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3335065ebeabfa21fee98f36b86d0849f384d90b9936a3cc5be0bec798b62ea9","last_reissued_at":"2026-05-18T01:22:10.300342Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:10.300342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.SR","nucl-ex"],"primary_cat":"nucl-th","authors_text":"H. B. Prosper, J. Piekarewicz, R. Utama","submitted_at":"2015-08-25T19:39:57Z","abstract_excerpt":"Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing \"state-of-the-art\" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.06263","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":""},"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":"1508.06263","created_at":"2026-05-18T01:22:10.300423+00:00"},{"alias_kind":"arxiv_version","alias_value":"1508.06263v1","created_at":"2026-05-18T01:22:10.300423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.06263","created_at":"2026-05-18T01:22:10.300423+00:00"},{"alias_kind":"pith_short_12","alias_value":"GM2QMXV6VP5C","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"GM2QMXV6VP5CD7XJ","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"GM2QMXV6","created_at":"2026-05-18T12:29:22.688609+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/GM2QMXV6VP5CD7XJR43LQ3IIJH","json":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH.json","graph_json":"https://pith.science/api/pith-number/GM2QMXV6VP5CD7XJR43LQ3IIJH/graph.json","events_json":"https://pith.science/api/pith-number/GM2QMXV6VP5CD7XJR43LQ3IIJH/events.json","paper":"https://pith.science/paper/GM2QMXV6"},"agent_actions":{"view_html":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH","download_json":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH.json","view_paper":"https://pith.science/paper/GM2QMXV6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1508.06263&json=true","fetch_graph":"https://pith.science/api/pith-number/GM2QMXV6VP5CD7XJR43LQ3IIJH/graph.json","fetch_events":"https://pith.science/api/pith-number/GM2QMXV6VP5CD7XJR43LQ3IIJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH/action/storage_attestation","attest_author":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH/action/author_attestation","sign_citation":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH/action/citation_signature","submit_replication":"https://pith.science/pith/GM2QMXV6VP5CD7XJR43LQ3IIJH/action/replication_record"}},"created_at":"2026-05-18T01:22:10.300423+00:00","updated_at":"2026-05-18T01:22:10.300423+00:00"}