{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CH4VDKCKKZDENE5YJ2TD5QWPTB","short_pith_number":"pith:CH4VDKCK","schema_version":"1.0","canonical_sha256":"11f951a84a56464693b84ea63ec2cf986def94e4d210e5a77f557496a046869c","source":{"kind":"arxiv","id":"2208.00106","version":1},"attestation_state":"computed","paper":{"title":"Accelerating Multi-Model Bayesian Inference, Model Selection and Systematic Studies for Gravitational Wave Astronomy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Charlie Hoy","submitted_at":"2022-07-29T23:50:45Z","abstract_excerpt":"Gravitational wave models are used to infer the properties of black holes in merging binaries from the observed gravitational wave signals through Bayesian inference. Although we have access to a large collection of signal models that are sufficiently accurate to infer the properties of black holes, for some signals, small discrepancies in the models lead to systematic differences in the inferred properties. In order to provide a single estimate for the properties of the black holes, it is preferable to marginalize over the model uncertainty. Bayesian model averaging is a commonly used techniq"},"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":"2208.00106","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2022-07-29T23:50:45Z","cross_cats_sorted":[],"title_canon_sha256":"0c75cd698a20524dcb8a3b3a6085bbe5592940362e491377f09e2a40e997c4fc","abstract_canon_sha256":"66c476df0b66b43eea8c011ac05dce6fb5028d1faf46da0ef7b991441e511e86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:07:50.086674Z","signature_b64":"4iYxMrnc6aIRMS4ZdB8nEm7N+0GBQyMbC0WVItTDi5oIlO5leV3LJamoj69Kh1gxhK+FkGG/4dZFyd1ex4XcDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11f951a84a56464693b84ea63ec2cf986def94e4d210e5a77f557496a046869c","last_reissued_at":"2026-07-05T05:07:50.086205Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:07:50.086205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Multi-Model Bayesian Inference, Model Selection and Systematic Studies for Gravitational Wave Astronomy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Charlie Hoy","submitted_at":"2022-07-29T23:50:45Z","abstract_excerpt":"Gravitational wave models are used to infer the properties of black holes in merging binaries from the observed gravitational wave signals through Bayesian inference. Although we have access to a large collection of signal models that are sufficiently accurate to infer the properties of black holes, for some signals, small discrepancies in the models lead to systematic differences in the inferred properties. In order to provide a single estimate for the properties of the black holes, it is preferable to marginalize over the model uncertainty. Bayesian model averaging is a commonly used techniq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00106","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.00106/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":"2208.00106","created_at":"2026-07-05T05:07:50.086262+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00106v1","created_at":"2026-07-05T05:07:50.086262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00106","created_at":"2026-07-05T05:07:50.086262+00:00"},{"alias_kind":"pith_short_12","alias_value":"CH4VDKCKKZDE","created_at":"2026-07-05T05:07:50.086262+00:00"},{"alias_kind":"pith_short_16","alias_value":"CH4VDKCKKZDENE5Y","created_at":"2026-07-05T05:07:50.086262+00:00"},{"alias_kind":"pith_short_8","alias_value":"CH4VDKCK","created_at":"2026-07-05T05:07:50.086262+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28716","citing_title":"Eccentric and unbound compact binaries in the LIGO-Virgo-KAGRA catalog: parameter estimation and waveform systematics with SEOBNRv6EHM","ref_index":136,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21859","citing_title":"Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2509.14849","citing_title":"A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2601.09678","citing_title":"The impact of waveform systematics and Gaussian noise on the interpretation of GW231123","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21859","citing_title":"Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data","ref_index":69,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB","json":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB.json","graph_json":"https://pith.science/api/pith-number/CH4VDKCKKZDENE5YJ2TD5QWPTB/graph.json","events_json":"https://pith.science/api/pith-number/CH4VDKCKKZDENE5YJ2TD5QWPTB/events.json","paper":"https://pith.science/paper/CH4VDKCK"},"agent_actions":{"view_html":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB","download_json":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB.json","view_paper":"https://pith.science/paper/CH4VDKCK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00106&json=true","fetch_graph":"https://pith.science/api/pith-number/CH4VDKCKKZDENE5YJ2TD5QWPTB/graph.json","fetch_events":"https://pith.science/api/pith-number/CH4VDKCKKZDENE5YJ2TD5QWPTB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB/action/storage_attestation","attest_author":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB/action/author_attestation","sign_citation":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB/action/citation_signature","submit_replication":"https://pith.science/pith/CH4VDKCKKZDENE5YJ2TD5QWPTB/action/replication_record"}},"created_at":"2026-07-05T05:07:50.086262+00:00","updated_at":"2026-07-05T05:07:50.086262+00:00"}