{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4KLEGIWFAZBJM4RF644LZOANR6","short_pith_number":"pith:4KLEGIWF","schema_version":"1.0","canonical_sha256":"e2964322c50642967225f738bcb80d8fbb97e2fd612773e0a026d0b8cdfd12d8","source":{"kind":"arxiv","id":"2412.03454","version":3},"attestation_state":"computed","paper":{"title":"Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Christopher Messenger, Ik Siong Heng, Jessica Irwin, John Veitch, Lami Suleiman, Qian Hu, Qi Sun","submitted_at":"2024-12-04T16:42:56Z","abstract_excerpt":"Gravitational waves (GWs) from binary neutron stars (BNSs) offer valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitude improvements in duration, detection rates, and signal strength. However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. To bridge this critical gap, we demonstrate a machine lea"},"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":"2412.03454","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2024-12-04T16:42:56Z","cross_cats_sorted":["astro-ph.HE","astro-ph.IM"],"title_canon_sha256":"57a79dd4f4ab82422e882c1a86663c04fd9c5a5b3b6e815f9b856cc23b99acae","abstract_canon_sha256":"4f95d32240b899065a93ae331a6bc4825c8d64257c81cb218c17c664f680e15a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:52.790192Z","signature_b64":"n6eoHdXxyGL5I876dWGsKzxAjg6I+CxtPr6Rj8voR4eKJmlAmgK0GxdV82y1j0f5Mlq1dUr7MPCJKrg70uryAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2964322c50642967225f738bcb80d8fbb97e2fd612773e0a026d0b8cdfd12d8","last_reissued_at":"2026-07-05T11:29:52.789712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:52.789712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Christopher Messenger, Ik Siong Heng, Jessica Irwin, John Veitch, Lami Suleiman, Qian Hu, Qi Sun","submitted_at":"2024-12-04T16:42:56Z","abstract_excerpt":"Gravitational waves (GWs) from binary neutron stars (BNSs) offer valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitude improvements in duration, detection rates, and signal strength. However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. To bridge this critical gap, we demonstrate a machine lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03454","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/2412.03454/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":"2412.03454","created_at":"2026-07-05T11:29:52.789778+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.03454v3","created_at":"2026-07-05T11:29:52.789778+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03454","created_at":"2026-07-05T11:29:52.789778+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KLEGIWFAZBJ","created_at":"2026-07-05T11:29:52.789778+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KLEGIWFAZBJM4RF","created_at":"2026-07-05T11:29:52.789778+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KLEGIWF","created_at":"2026-07-05T11:29:52.789778+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02690","citing_title":"Speed and accuracy for long signals: Frequency-domain effective-one-body waveforms for compact binary coalescences","ref_index":99,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14270","citing_title":"Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6","json":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6.json","graph_json":"https://pith.science/api/pith-number/4KLEGIWFAZBJM4RF644LZOANR6/graph.json","events_json":"https://pith.science/api/pith-number/4KLEGIWFAZBJM4RF644LZOANR6/events.json","paper":"https://pith.science/paper/4KLEGIWF"},"agent_actions":{"view_html":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6","download_json":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6.json","view_paper":"https://pith.science/paper/4KLEGIWF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.03454&json=true","fetch_graph":"https://pith.science/api/pith-number/4KLEGIWFAZBJM4RF644LZOANR6/graph.json","fetch_events":"https://pith.science/api/pith-number/4KLEGIWFAZBJM4RF644LZOANR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6/action/storage_attestation","attest_author":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6/action/author_attestation","sign_citation":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6/action/citation_signature","submit_replication":"https://pith.science/pith/4KLEGIWFAZBJM4RF644LZOANR6/action/replication_record"}},"created_at":"2026-07-05T11:29:52.789778+00:00","updated_at":"2026-07-05T11:29:52.789778+00:00"}