{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VE3SLFVU2UNE6VYHYBGD7RJF2J","short_pith_number":"pith:VE3SLFVU","schema_version":"1.0","canonical_sha256":"a9372596b4d51a4f5707c04c3fc525d266b316fe866f39dcfdfb14ad70fde228","source":{"kind":"arxiv","id":"2608.00281","version":1},"attestation_state":"computed","paper":{"title":"Can machine learning improve the detectability and disentanglement of the gravitational-wave background?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Hugo Einsle, Jishnu Suresh, Mairi Sakellariadou, Marie Anne Bizouard, Tania Regimbau","submitted_at":"2026-07-31T20:34:25Z","abstract_excerpt":"Gravitational waves from compact binary coalescences and from early Universe processes are expected to form a gravitational-wave background. We employ a custom deep learning multi-scale multi-headed autoencoder architecture to isolate gravitational-wave background from detector noise, followed by a Markov chain Monte Carlo inference stage to separate the astrophysical and cosmological components. Analyzing $108$-day mock datasets representative of the first period of the fourth LIGO-Virgo-KAGRA observing run, we show that we can detect with high confidence --- $\\log_{10}$ noise Bayes factor la"},"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":"2608.00281","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2026-07-31T20:34:25Z","cross_cats_sorted":["astro-ph.HE","astro-ph.IM"],"title_canon_sha256":"fcef4a126b01061a1231ed9878b82baa3b3a6668af41f7ad53866b867a5eb375","abstract_canon_sha256":"1b3d5dc03a814eb282606469df32936f643378f8299c76bd7df2398f3cd6f754"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T00:34:44.122372Z","signature_b64":"IkyveK7VPetu3Z45S+4Qmo0OvkyLhoBTuA0h0mGACZAIQAvd175d8B6x6UsbhbqMg7RRQsWk3EYyG5vIUBbqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9372596b4d51a4f5707c04c3fc525d266b316fe866f39dcfdfb14ad70fde228","last_reissued_at":"2026-08-04T00:34:44.120542Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T00:34:44.120542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can machine learning improve the detectability and disentanglement of the gravitational-wave background?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Hugo Einsle, Jishnu Suresh, Mairi Sakellariadou, Marie Anne Bizouard, Tania Regimbau","submitted_at":"2026-07-31T20:34:25Z","abstract_excerpt":"Gravitational waves from compact binary coalescences and from early Universe processes are expected to form a gravitational-wave background. We employ a custom deep learning multi-scale multi-headed autoencoder architecture to isolate gravitational-wave background from detector noise, followed by a Markov chain Monte Carlo inference stage to separate the astrophysical and cosmological components. Analyzing $108$-day mock datasets representative of the first period of the fourth LIGO-Virgo-KAGRA observing run, we show that we can detect with high confidence --- $\\log_{10}$ noise Bayes factor la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00281","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/2608.00281/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":"2608.00281","created_at":"2026-08-04T00:34:44.121808+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.00281v1","created_at":"2026-08-04T00:34:44.121808+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00281","created_at":"2026-08-04T00:34:44.121808+00:00"},{"alias_kind":"pith_short_12","alias_value":"VE3SLFVU2UNE","created_at":"2026-08-04T00:34:44.121808+00:00"},{"alias_kind":"pith_short_16","alias_value":"VE3SLFVU2UNE6VYH","created_at":"2026-08-04T00:34:44.121808+00:00"},{"alias_kind":"pith_short_8","alias_value":"VE3SLFVU","created_at":"2026-08-04T00:34:44.121808+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J","json":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J.json","graph_json":"https://pith.science/api/pith-number/VE3SLFVU2UNE6VYHYBGD7RJF2J/graph.json","events_json":"https://pith.science/api/pith-number/VE3SLFVU2UNE6VYHYBGD7RJF2J/events.json","paper":"https://pith.science/paper/VE3SLFVU"},"agent_actions":{"view_html":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J","download_json":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J.json","view_paper":"https://pith.science/paper/VE3SLFVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.00281&json=true","fetch_graph":"https://pith.science/api/pith-number/VE3SLFVU2UNE6VYHYBGD7RJF2J/graph.json","fetch_events":"https://pith.science/api/pith-number/VE3SLFVU2UNE6VYHYBGD7RJF2J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J/action/storage_attestation","attest_author":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J/action/author_attestation","sign_citation":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J/action/citation_signature","submit_replication":"https://pith.science/pith/VE3SLFVU2UNE6VYHYBGD7RJF2J/action/replication_record"}},"created_at":"2026-08-04T00:34:44.121808+00:00","updated_at":"2026-08-04T00:34:44.121808+00:00"}