{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NSW25TYHT2NHXECGPGEGS5S2TW","short_pith_number":"pith:NSW25TYH","schema_version":"1.0","canonical_sha256":"6cadaecf079e9a7b9046798869765a9db9a65ee7f92d2b8ac0541dd4bcd2b54c","source":{"kind":"arxiv","id":"1706.07446","version":1},"attestation_state":"computed","paper":{"title":"Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","cs.CV","cs.LG","cs.NE"],"primary_cat":"gr-qc","authors_text":"Daniel George, E. A. Huerta, Hongyu Shen","submitted_at":"2017-06-22T18:11:13Z","abstract_excerpt":"The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals. The sophisticated design of these detectors mitigates the effect of most types of noise. However, advanced LIGO data streams are contaminated by numerous artifacts known as glitches: non-Gaussian noise transients with complex morphologies. Given their high rate of occurrence, glitches can lead to false coincident detections, obscure and even mimic gravitational wave signals. Therefore, successfully characterizing and removing glitches from advanced LIGO data is of utmost i"},"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":"1706.07446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2017-06-22T18:11:13Z","cross_cats_sorted":["astro-ph.IM","cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"c6dfd15a9d081f6effe360e431a8b486128270c17cedbeb362d2b9f9cd937610","abstract_canon_sha256":"2f35fda45b1fbea77f49e5392b6e740130e431f12e4cd9ca242c646749bd8258"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:52.593897Z","signature_b64":"2qjL1MhfjzgyGn5foXkGZHU/d9lVAPW9Y0D4Dh5QmaZ/3iugW7P/BKRR31rzdfCMF9aY6UfqXE0o+skmlTvtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6cadaecf079e9a7b9046798869765a9db9a65ee7f92d2b8ac0541dd4bcd2b54c","last_reissued_at":"2026-05-18T00:10:52.593197Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:52.593197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","cs.CV","cs.LG","cs.NE"],"primary_cat":"gr-qc","authors_text":"Daniel George, E. A. Huerta, Hongyu Shen","submitted_at":"2017-06-22T18:11:13Z","abstract_excerpt":"The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals. The sophisticated design of these detectors mitigates the effect of most types of noise. However, advanced LIGO data streams are contaminated by numerous artifacts known as glitches: non-Gaussian noise transients with complex morphologies. Given their high rate of occurrence, glitches can lead to false coincident detections, obscure and even mimic gravitational wave signals. Therefore, successfully characterizing and removing glitches from advanced LIGO data is of utmost i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.07446","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":"1706.07446","created_at":"2026-05-18T00:10:52.593325+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.07446v1","created_at":"2026-05-18T00:10:52.593325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.07446","created_at":"2026-05-18T00:10:52.593325+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSW25TYHT2NH","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSW25TYHT2NHXECG","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSW25TYH","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13687","citing_title":"VIGILant: an automatic classification pipeline for glitches in the Virgo detector","ref_index":35,"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":86,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW","json":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW.json","graph_json":"https://pith.science/api/pith-number/NSW25TYHT2NHXECGPGEGS5S2TW/graph.json","events_json":"https://pith.science/api/pith-number/NSW25TYHT2NHXECGPGEGS5S2TW/events.json","paper":"https://pith.science/paper/NSW25TYH"},"agent_actions":{"view_html":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW","download_json":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW.json","view_paper":"https://pith.science/paper/NSW25TYH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.07446&json=true","fetch_graph":"https://pith.science/api/pith-number/NSW25TYHT2NHXECGPGEGS5S2TW/graph.json","fetch_events":"https://pith.science/api/pith-number/NSW25TYHT2NHXECGPGEGS5S2TW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW/action/storage_attestation","attest_author":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW/action/author_attestation","sign_citation":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW/action/citation_signature","submit_replication":"https://pith.science/pith/NSW25TYHT2NHXECGPGEGS5S2TW/action/replication_record"}},"created_at":"2026-05-18T00:10:52.593325+00:00","updated_at":"2026-05-18T00:10:52.593325+00:00"}