{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:DECU3YANBKP4M6S6NHPNIRMMPM","short_pith_number":"pith:DECU3YAN","schema_version":"1.0","canonical_sha256":"19054de00d0a9fc67a5e69ded4458c7b1a897366e262f3d91d151051ab4a0e56","source":{"kind":"arxiv","id":"1207.4956","version":1},"attestation_state":"computed","paper":{"title":"EMRI data analysis with a phenomenological waveform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Stanislav Babak, Yan Wang, Yu Shang","submitted_at":"2012-07-20T14:01:55Z","abstract_excerpt":"Extreme mass ratio inspirals (EMRIs) (capture and inspiral of a compact stellar mass object into a Massive Black Hole (MBH)) are among the most interesting objects for the gravitational wave astronomy. It is a very challenging task to detect those sources with the accurate estimation parameters of binaries primarily due to a large number of the secondary maxima on the likelihood surface. Search algorithms based on the matched filtering require computation of the gravitational waveform hundreds of thousands of times, which is currently not feasible with the most accurate (faithful) models of EM"},"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":"1207.4956","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2012-07-20T14:01:55Z","cross_cats_sorted":[],"title_canon_sha256":"cebd4e49ba854cb315c5fb4e7fef8879980d519da19b3f4522e4b1d9a7337e32","abstract_canon_sha256":"55ec17ceaea457bae466dc37140b44ca048981379cc6e88585533997aa7b15b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:09.594903Z","signature_b64":"GUdv783TjWJUmznscOHUyelE3PaBeMNpOAw3tzQhZa6gitdnhFWHd30EXI7bpww3dTLN86izGPrdzgLqtkSSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19054de00d0a9fc67a5e69ded4458c7b1a897366e262f3d91d151051ab4a0e56","last_reissued_at":"2026-05-18T02:43:09.594540Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:09.594540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EMRI data analysis with a phenomenological waveform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Stanislav Babak, Yan Wang, Yu Shang","submitted_at":"2012-07-20T14:01:55Z","abstract_excerpt":"Extreme mass ratio inspirals (EMRIs) (capture and inspiral of a compact stellar mass object into a Massive Black Hole (MBH)) are among the most interesting objects for the gravitational wave astronomy. It is a very challenging task to detect those sources with the accurate estimation parameters of binaries primarily due to a large number of the secondary maxima on the likelihood surface. Search algorithms based on the matched filtering require computation of the gravitational waveform hundreds of thousands of times, which is currently not feasible with the most accurate (faithful) models of EM"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.4956","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":"1207.4956","created_at":"2026-05-18T02:43:09.594593+00:00"},{"alias_kind":"arxiv_version","alias_value":"1207.4956v1","created_at":"2026-05-18T02:43:09.594593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.4956","created_at":"2026-05-18T02:43:09.594593+00:00"},{"alias_kind":"pith_short_12","alias_value":"DECU3YANBKP4","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_16","alias_value":"DECU3YANBKP4M6S6","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_8","alias_value":"DECU3YAN","created_at":"2026-05-18T12:27:01.376967+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"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":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM","json":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM.json","graph_json":"https://pith.science/api/pith-number/DECU3YANBKP4M6S6NHPNIRMMPM/graph.json","events_json":"https://pith.science/api/pith-number/DECU3YANBKP4M6S6NHPNIRMMPM/events.json","paper":"https://pith.science/paper/DECU3YAN"},"agent_actions":{"view_html":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM","download_json":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM.json","view_paper":"https://pith.science/paper/DECU3YAN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1207.4956&json=true","fetch_graph":"https://pith.science/api/pith-number/DECU3YANBKP4M6S6NHPNIRMMPM/graph.json","fetch_events":"https://pith.science/api/pith-number/DECU3YANBKP4M6S6NHPNIRMMPM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM/action/storage_attestation","attest_author":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM/action/author_attestation","sign_citation":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM/action/citation_signature","submit_replication":"https://pith.science/pith/DECU3YANBKP4M6S6NHPNIRMMPM/action/replication_record"}},"created_at":"2026-05-18T02:43:09.594593+00:00","updated_at":"2026-05-18T02:43:09.594593+00:00"}