{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4JYJ25DYXEMNDFFKBFPCPRCVAC","short_pith_number":"pith:4JYJ25DY","schema_version":"1.0","canonical_sha256":"e2709d7478b918d194aa095e27c4550097af7faba43797abdd6997423c4a3dd4","source":{"kind":"arxiv","id":"2309.04303","version":2},"attestation_state":"computed","paper":{"title":"Fast Bayesian gravitational wave parameter estimation using convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","physics.comp-ph"],"primary_cat":"gr-qc","authors_text":"Ll.M. Mir, M. Andr\\'es-Carcasona, M. Martinez","submitted_at":"2023-09-08T13:04:34Z","abstract_excerpt":"The determination of the physical parameters of gravitational wave events is a fundamental pillar in the analysis of the signals observed by the current ground-based interferometers. Typically, this is done using Bayesian inference approaches which, albeit very accurate, are very computationally expensive. We propose a convolutional neural network approach to perform this task. The convolutional neural network is trained using simulated signals injected in a Gaussian noise. We verify the correctness of the neural network's output distribution and compare its estimates with the posterior distri"},"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":"2309.04303","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2023-09-08T13:04:34Z","cross_cats_sorted":["astro-ph.IM","physics.comp-ph"],"title_canon_sha256":"23d3e4cb3d807dccfb84e2bdd7207a615d712066d402259d1fb40797b0ce7dbf","abstract_canon_sha256":"02dacf23d7a6245875a9f67f6ddfc913cc0e4d8f3678c35777691b55706707a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:31.112258Z","signature_b64":"Z7OCjcUj9U4bjjFDAEf6DhCZ/0OrmEvBxs7cxu8nAevuLgo5B6ywFfcelRJBC4DASI2oaYXNDwjuVRJaQSDlDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2709d7478b918d194aa095e27c4550097af7faba43797abdd6997423c4a3dd4","last_reissued_at":"2026-07-05T07:09:31.111677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:31.111677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Bayesian gravitational wave parameter estimation using convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","physics.comp-ph"],"primary_cat":"gr-qc","authors_text":"Ll.M. Mir, M. Andr\\'es-Carcasona, M. Martinez","submitted_at":"2023-09-08T13:04:34Z","abstract_excerpt":"The determination of the physical parameters of gravitational wave events is a fundamental pillar in the analysis of the signals observed by the current ground-based interferometers. Typically, this is done using Bayesian inference approaches which, albeit very accurate, are very computationally expensive. We propose a convolutional neural network approach to perform this task. The convolutional neural network is trained using simulated signals injected in a Gaussian noise. We verify the correctness of the neural network's output distribution and compare its estimates with the posterior distri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.04303","kind":"arxiv","version":2},"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/2309.04303/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":"2309.04303","created_at":"2026-07-05T07:09:31.111753+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.04303v2","created_at":"2026-07-05T07:09:31.111753+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.04303","created_at":"2026-07-05T07:09:31.111753+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JYJ25DYXEMN","created_at":"2026-07-05T07:09:31.111753+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JYJ25DYXEMNDFFK","created_at":"2026-07-05T07:09:31.111753+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JYJ25DY","created_at":"2026-07-05T07:09:31.111753+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.22462","citing_title":"Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective","ref_index":270,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC","json":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC.json","graph_json":"https://pith.science/api/pith-number/4JYJ25DYXEMNDFFKBFPCPRCVAC/graph.json","events_json":"https://pith.science/api/pith-number/4JYJ25DYXEMNDFFKBFPCPRCVAC/events.json","paper":"https://pith.science/paper/4JYJ25DY"},"agent_actions":{"view_html":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC","download_json":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC.json","view_paper":"https://pith.science/paper/4JYJ25DY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.04303&json=true","fetch_graph":"https://pith.science/api/pith-number/4JYJ25DYXEMNDFFKBFPCPRCVAC/graph.json","fetch_events":"https://pith.science/api/pith-number/4JYJ25DYXEMNDFFKBFPCPRCVAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC/action/storage_attestation","attest_author":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC/action/author_attestation","sign_citation":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC/action/citation_signature","submit_replication":"https://pith.science/pith/4JYJ25DYXEMNDFFKBFPCPRCVAC/action/replication_record"}},"created_at":"2026-07-05T07:09:31.111753+00:00","updated_at":"2026-07-05T07:09:31.111753+00:00"}