{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XGCPACRNED6ORZPBIJLXFSHOEE","short_pith_number":"pith:XGCPACRN","schema_version":"1.0","canonical_sha256":"b984f00a2d20fce8e5e1425772c8ee2125a0cb936fe90ec319dacf3eb3cd02d4","source":{"kind":"arxiv","id":"2602.22074","version":1},"attestation_state":"computed","paper":{"title":"Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Argyro Sasli, Michael W. Coughlin, Minas Karamanis, Nikolaos Karnesis, Nikolaos Stergioulas, Uro\\v{s} Seljak, Vuk Mandic","submitted_at":"2026-02-25T16:25:00Z","abstract_excerpt":"Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case"},"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":"2602.22074","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2026-02-25T16:25:00Z","cross_cats_sorted":[],"title_canon_sha256":"fc1695b8c3ace37b95ee0bc2b807b82fa22cbf65ab75b1e09a4ee2cdfc6adf69","abstract_canon_sha256":"3b2c7c5c48cf26a4d96190280966ac08e49bbaca591764c3f8f53f132a4053e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:18:35.706142Z","signature_b64":"BZZjEnFnb1sRBRaRWxh5kKkJAvwgJ1dpIaPhggoHOT/xXvonKumc9gmrJiHChtEu0mhJhcmKjcvMcIJp5EzNCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b984f00a2d20fce8e5e1425772c8ee2125a0cb936fe90ec319dacf3eb3cd02d4","last_reissued_at":"2026-07-07T02:18:35.705216Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:18:35.705216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Argyro Sasli, Michael W. Coughlin, Minas Karamanis, Nikolaos Karnesis, Nikolaos Stergioulas, Uro\\v{s} Seljak, Vuk Mandic","submitted_at":"2026-02-25T16:25:00Z","abstract_excerpt":"Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.22074","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/2602.22074/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":"2602.22074","created_at":"2026-07-07T02:18:35.705334+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.22074v1","created_at":"2026-07-07T02:18:35.705334+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.22074","created_at":"2026-07-07T02:18:35.705334+00:00"},{"alias_kind":"pith_short_12","alias_value":"XGCPACRNED6O","created_at":"2026-07-07T02:18:35.705334+00:00"},{"alias_kind":"pith_short_16","alias_value":"XGCPACRNED6ORZPB","created_at":"2026-07-07T02:18:35.705334+00:00"},{"alias_kind":"pith_short_8","alias_value":"XGCPACRN","created_at":"2026-07-07T02:18:35.705334+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.31304","citing_title":"A parametric signal plus noise inference framework for short duration non-Gaussian noise transients","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE","json":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE.json","graph_json":"https://pith.science/api/pith-number/XGCPACRNED6ORZPBIJLXFSHOEE/graph.json","events_json":"https://pith.science/api/pith-number/XGCPACRNED6ORZPBIJLXFSHOEE/events.json","paper":"https://pith.science/paper/XGCPACRN"},"agent_actions":{"view_html":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE","download_json":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE.json","view_paper":"https://pith.science/paper/XGCPACRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.22074&json=true","fetch_graph":"https://pith.science/api/pith-number/XGCPACRNED6ORZPBIJLXFSHOEE/graph.json","fetch_events":"https://pith.science/api/pith-number/XGCPACRNED6ORZPBIJLXFSHOEE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE/action/storage_attestation","attest_author":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE/action/author_attestation","sign_citation":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE/action/citation_signature","submit_replication":"https://pith.science/pith/XGCPACRNED6ORZPBIJLXFSHOEE/action/replication_record"}},"created_at":"2026-07-07T02:18:35.705334+00:00","updated_at":"2026-07-07T02:18:35.705334+00:00"}