{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SDKDBYGE6SKJ623A4MWYJYPK4P","short_pith_number":"pith:SDKDBYGE","schema_version":"1.0","canonical_sha256":"90d430e0c4f4949f6b60e32d84e1eae3cfc612e3cb621f295344bc7c6bb36c83","source":{"kind":"arxiv","id":"2401.12913","version":2},"attestation_state":"computed","paper":{"title":"Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO's Fourth Observing Run","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","eess.IV"],"primary_cat":"gr-qc","authors_text":"Aggelos K. Katsaggelos, Carsten {\\O}sterlund, Christopher P. L. Berry, Corey B. Jackson, Kevin Crowston, Michael Zevin, Sharan Banagiri, Vicky Kalogera, Yunan Wu, Zoheyr Doctor","submitted_at":"2024-01-23T17:06:13Z","abstract_excerpt":"The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO"},"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":"2401.12913","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2024-01-23T17:06:13Z","cross_cats_sorted":["astro-ph.IM","eess.IV"],"title_canon_sha256":"6648d17ff183e6c4d0a4883cba5acb4914da14a04bec3f7040f5caa43a1a506c","abstract_canon_sha256":"bbbf78cf3783b32efb8e79c66c7f2a9e05015ae1feebd7b08f4b17e44d0e64b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:40.075389Z","signature_b64":"WvMrQGCg278LI5R5lTA3NwNeO0OaO5x7BMry0vPOI/16e+beNND1iJRyI1PZ7WjLZ/w3EicJ+vC9tkuEuDRnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90d430e0c4f4949f6b60e32d84e1eae3cfc612e3cb621f295344bc7c6bb36c83","last_reissued_at":"2026-07-05T11:53:40.074894Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:40.074894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO's Fourth Observing Run","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","eess.IV"],"primary_cat":"gr-qc","authors_text":"Aggelos K. Katsaggelos, Carsten {\\O}sterlund, Christopher P. L. Berry, Corey B. Jackson, Kevin Crowston, Michael Zevin, Sharan Banagiri, Vicky Kalogera, Yunan Wu, Zoheyr Doctor","submitted_at":"2024-01-23T17:06:13Z","abstract_excerpt":"The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.12913","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/2401.12913/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":"2401.12913","created_at":"2026-07-05T11:53:40.074951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.12913v2","created_at":"2026-07-05T11:53:40.074951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.12913","created_at":"2026-07-05T11:53:40.074951+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDKDBYGE6SKJ","created_at":"2026-07-05T11:53:40.074951+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDKDBYGE6SKJ623A","created_at":"2026-07-05T11:53:40.074951+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDKDBYGE","created_at":"2026-07-05T11:53:40.074951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27227","citing_title":"Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27227","citing_title":"Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2508.13923","citing_title":"Hunting for new glitches in LIGO data using community science","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13687","citing_title":"VIGILant: an automatic classification pipeline for glitches in the Virgo detector","ref_index":27,"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":120,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P","json":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P.json","graph_json":"https://pith.science/api/pith-number/SDKDBYGE6SKJ623A4MWYJYPK4P/graph.json","events_json":"https://pith.science/api/pith-number/SDKDBYGE6SKJ623A4MWYJYPK4P/events.json","paper":"https://pith.science/paper/SDKDBYGE"},"agent_actions":{"view_html":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P","download_json":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P.json","view_paper":"https://pith.science/paper/SDKDBYGE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.12913&json=true","fetch_graph":"https://pith.science/api/pith-number/SDKDBYGE6SKJ623A4MWYJYPK4P/graph.json","fetch_events":"https://pith.science/api/pith-number/SDKDBYGE6SKJ623A4MWYJYPK4P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P/action/storage_attestation","attest_author":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P/action/author_attestation","sign_citation":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P/action/citation_signature","submit_replication":"https://pith.science/pith/SDKDBYGE6SKJ623A4MWYJYPK4P/action/replication_record"}},"created_at":"2026-07-05T11:53:40.074951+00:00","updated_at":"2026-07-05T11:53:40.074951+00:00"}