{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EAV7UJLCCBRCADIAUVJ5WGRRFQ","short_pith_number":"pith:EAV7UJLC","schema_version":"1.0","canonical_sha256":"202bfa25621062200d00a553db1a312c0b3007f2e02bd2e207054beaaed32a2e","source":{"kind":"arxiv","id":"2303.05986","version":1},"attestation_state":"computed","paper":{"title":"An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Francesco Salemi, Gabriele Vedovato, Giovanni Andrea Prodi, Marco Drago, Sophie Bini","submitted_at":"2023-03-10T15:42:03Z","abstract_excerpt":"The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification"},"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":"2303.05986","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2023-03-10T15:42:03Z","cross_cats_sorted":[],"title_canon_sha256":"b46b529f4c1604dad3ad943488186c57ced806a5b1ac2ed5839941c0f5c6a4e2","abstract_canon_sha256":"838c1d8ba3de27175b1e87a9cb95ea182b7db6ee22aeba9b3a4ff49e48af5177"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:22:01.225409Z","signature_b64":"G8xmkTQuVUXseT5ff5Hf1f/LCNFl++4XZ0nddGJI+Vjt7PZ+b3te5XqCPB9thfRZX3h7zibY5h1xE4x+V2G4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"202bfa25621062200d00a553db1a312c0b3007f2e02bd2e207054beaaed32a2e","last_reissued_at":"2026-07-05T06:22:01.224970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:22:01.224970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"gr-qc","authors_text":"Francesco Salemi, Gabriele Vedovato, Giovanni Andrea Prodi, Marco Drago, Sophie Bini","submitted_at":"2023-03-10T15:42:03Z","abstract_excerpt":"The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05986","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/2303.05986/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":"2303.05986","created_at":"2026-07-05T06:22:01.225026+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05986v1","created_at":"2026-07-05T06:22:01.225026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05986","created_at":"2026-07-05T06:22:01.225026+00:00"},{"alias_kind":"pith_short_12","alias_value":"EAV7UJLCCBRC","created_at":"2026-07-05T06:22:01.225026+00:00"},{"alias_kind":"pith_short_16","alias_value":"EAV7UJLCCBRCADIA","created_at":"2026-07-05T06:22:01.225026+00:00"},{"alias_kind":"pith_short_8","alias_value":"EAV7UJLC","created_at":"2026-07-05T06:22:01.225026+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05283","citing_title":"Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ","json":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ.json","graph_json":"https://pith.science/api/pith-number/EAV7UJLCCBRCADIAUVJ5WGRRFQ/graph.json","events_json":"https://pith.science/api/pith-number/EAV7UJLCCBRCADIAUVJ5WGRRFQ/events.json","paper":"https://pith.science/paper/EAV7UJLC"},"agent_actions":{"view_html":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ","download_json":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ.json","view_paper":"https://pith.science/paper/EAV7UJLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05986&json=true","fetch_graph":"https://pith.science/api/pith-number/EAV7UJLCCBRCADIAUVJ5WGRRFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/EAV7UJLCCBRCADIAUVJ5WGRRFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ/action/storage_attestation","attest_author":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ/action/author_attestation","sign_citation":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ/action/citation_signature","submit_replication":"https://pith.science/pith/EAV7UJLCCBRCADIAUVJ5WGRRFQ/action/replication_record"}},"created_at":"2026-07-05T06:22:01.225026+00:00","updated_at":"2026-07-05T06:22:01.225026+00:00"}