{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2TYP74APNZI5QMARRCWXAGPNH2","short_pith_number":"pith:2TYP74AP","schema_version":"1.0","canonical_sha256":"d4f0fff00f6e51d8301188ad7019ed3eb6cf486fd66aa1b651fad989cb4787c3","source":{"kind":"arxiv","id":"2310.13405","version":2},"attestation_state":"computed","paper":{"title":"Cosmological Inference using Gravitational Waves and Normalising Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Christopher Messenger, Federico Stachurski, Martin Hendry","submitted_at":"2023-10-20T10:23:41Z","abstract_excerpt":"We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and relies on the generation of training data representing distributions of gravitational wave event parameters. These parameters are conditional on the underlying cosmology and incorporate prior information from galaxy catalogues. We provide an example analysis inferring the Hubble constant using binary black holes detected during the O1, O2, and O3 observational ru"},"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":"2310.13405","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2023-10-20T10:23:41Z","cross_cats_sorted":[],"title_canon_sha256":"4419e44f4a23cf64eb96c433df89e57b98c74b1d178f85c4066e6e59b26b1b0d","abstract_canon_sha256":"00d5ea8e395b95a113a69ecac2c8970dc91089d28a8818d63c03d2a03571b5a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:51.791750Z","signature_b64":"31rydVj8UmE+R23Np5trnsbOurnJ/f6i3iJJHOhseDQ7OVNzZBntZv5HP/PHVRDeczSSHwXhAnwGevKp/zrkBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4f0fff00f6e51d8301188ad7019ed3eb6cf486fd66aa1b651fad989cb4787c3","last_reissued_at":"2026-07-05T07:04:51.791296Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:51.791296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cosmological Inference using Gravitational Waves and Normalising Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Christopher Messenger, Federico Stachurski, Martin Hendry","submitted_at":"2023-10-20T10:23:41Z","abstract_excerpt":"We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and relies on the generation of training data representing distributions of gravitational wave event parameters. These parameters are conditional on the underlying cosmology and incorporate prior information from galaxy catalogues. We provide an example analysis inferring the Hubble constant using binary black holes detected during the O1, O2, and O3 observational ru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.13405","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/2310.13405/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":"2310.13405","created_at":"2026-07-05T07:04:51.791366+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.13405v2","created_at":"2026-07-05T07:04:51.791366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.13405","created_at":"2026-07-05T07:04:51.791366+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TYP74APNZI5","created_at":"2026-07-05T07:04:51.791366+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TYP74APNZI5QMAR","created_at":"2026-07-05T07:04:51.791366+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TYP74AP","created_at":"2026-07-05T07:04:51.791366+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26581","citing_title":"Normalizing flows for density estimation in multi-detector gravitational-wave searches","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2","json":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2.json","graph_json":"https://pith.science/api/pith-number/2TYP74APNZI5QMARRCWXAGPNH2/graph.json","events_json":"https://pith.science/api/pith-number/2TYP74APNZI5QMARRCWXAGPNH2/events.json","paper":"https://pith.science/paper/2TYP74AP"},"agent_actions":{"view_html":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2","download_json":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2.json","view_paper":"https://pith.science/paper/2TYP74AP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.13405&json=true","fetch_graph":"https://pith.science/api/pith-number/2TYP74APNZI5QMARRCWXAGPNH2/graph.json","fetch_events":"https://pith.science/api/pith-number/2TYP74APNZI5QMARRCWXAGPNH2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2/action/storage_attestation","attest_author":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2/action/author_attestation","sign_citation":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2/action/citation_signature","submit_replication":"https://pith.science/pith/2TYP74APNZI5QMARRCWXAGPNH2/action/replication_record"}},"created_at":"2026-07-05T07:04:51.791366+00:00","updated_at":"2026-07-05T07:04:51.791366+00:00"}