{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HTAN7J7BRD7MVUU2VYPF54YBQX","short_pith_number":"pith:HTAN7J7B","schema_version":"1.0","canonical_sha256":"3cc0dfa7e188fecad29aae1e5ef30185ecebe4fff621b1b5667e4b0eeb63a7b9","source":{"kind":"arxiv","id":"2505.00657","version":2},"attestation_state":"computed","paper":{"title":"Joint inference for gravitational wave signals and glitches using a data-informed glitch model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Ann-Kristin Malz, John Veitch","submitted_at":"2025-05-01T16:59:36Z","abstract_excerpt":"Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inf"},"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":"2505.00657","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2025-05-01T16:59:36Z","cross_cats_sorted":["astro-ph.HE","astro-ph.IM"],"title_canon_sha256":"ca68df569b6b55c62bfe84be3cfa5eb9409edf24de52983cbcd5714ff4cdf701","abstract_canon_sha256":"e4e35b23960d9f76159b5d1bdc4cfb4934ee874e02b789e375300f2f66ace81f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:57.887976Z","signature_b64":"nG76bC+rOVlcR8YZW/QSb5i93UvwCde0QFZCqk4+axi/vX8L0RB+BXRK3PgOJ8ZCtjGwnGC0kGPww9jg6LvRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cc0dfa7e188fecad29aae1e5ef30185ecebe4fff621b1b5667e4b0eeb63a7b9","last_reissued_at":"2026-07-05T11:45:57.887421Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:57.887421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint inference for gravitational wave signals and glitches using a data-informed glitch model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Ann-Kristin Malz, John Veitch","submitted_at":"2025-05-01T16:59:36Z","abstract_excerpt":"Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00657","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/2505.00657/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":"2505.00657","created_at":"2026-07-05T11:45:57.887485+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.00657v2","created_at":"2026-07-05T11:45:57.887485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00657","created_at":"2026-07-05T11:45:57.887485+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTAN7J7BRD7M","created_at":"2026-07-05T11:45:57.887485+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTAN7J7BRD7MVUU2","created_at":"2026-07-05T11:45:57.887485+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTAN7J7B","created_at":"2026-07-05T11:45:57.887485+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.13923","citing_title":"Hunting for new glitches in LIGO data using community science","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13867","citing_title":"Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows","ref_index":72,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX","json":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX.json","graph_json":"https://pith.science/api/pith-number/HTAN7J7BRD7MVUU2VYPF54YBQX/graph.json","events_json":"https://pith.science/api/pith-number/HTAN7J7BRD7MVUU2VYPF54YBQX/events.json","paper":"https://pith.science/paper/HTAN7J7B"},"agent_actions":{"view_html":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX","download_json":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX.json","view_paper":"https://pith.science/paper/HTAN7J7B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.00657&json=true","fetch_graph":"https://pith.science/api/pith-number/HTAN7J7BRD7MVUU2VYPF54YBQX/graph.json","fetch_events":"https://pith.science/api/pith-number/HTAN7J7BRD7MVUU2VYPF54YBQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX/action/storage_attestation","attest_author":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX/action/author_attestation","sign_citation":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX/action/citation_signature","submit_replication":"https://pith.science/pith/HTAN7J7BRD7MVUU2VYPF54YBQX/action/replication_record"}},"created_at":"2026-07-05T11:45:57.887485+00:00","updated_at":"2026-07-05T11:45:57.887485+00:00"}