{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WYF4552RYVVJXLWTYYQHG3Y465","short_pith_number":"pith:WYF4552R","schema_version":"1.0","canonical_sha256":"b60bcef751c56a9baed3c620736f1cf75f4623702899a6850f9c99e8aec89f2d","source":{"kind":"arxiv","id":"2004.01050","version":2},"attestation_state":"computed","paper":{"title":"Deep learning merger masses estimation from gravitational waves signals in the frequency domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Antonio Enea Romano, Camilo Santa, Juan Pablo Marulanda","submitted_at":"2020-04-02T14:47:37Z","abstract_excerpt":"Detection of gravitational waves (GW) from compact binary mergers provide a new window into multi-messenger astrophysics. The standard technique to determine the merger parameters is matched filtering, consisting in comparing the signal to a template bank. This approach can be time consuming and computationally expensive due to the large amount of experimental data which needs to be analyzed.\n  In the attempt to find more efficient data analysis methods we develop a new frequency domain convolutional neural network (FCNN) to predict the merger masses from the spectrogram of the detector signal"},"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":"2004.01050","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"gr-qc","submitted_at":"2020-04-02T14:47:37Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"7e85d7b4e4cdc54c16622cc2e7d0877d926ac0d2ca9bde8092d2b4be2d47cada","abstract_canon_sha256":"c465e29cf2520aefd14782594aa3369175797cca288c487339bafe3d6d894df7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:46:17.615571Z","signature_b64":"Y777UZsI6vNP4itGRfShG3NwvMU7Psd6GaUBPOxsDm83KcRnOScAtnSV0EkjrCF0tm8qBOJBUnoKp50PJwsDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b60bcef751c56a9baed3c620736f1cf75f4623702899a6850f9c99e8aec89f2d","last_reissued_at":"2026-07-05T01:46:17.615220Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:46:17.615220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning merger masses estimation from gravitational waves signals in the frequency domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"gr-qc","authors_text":"Antonio Enea Romano, Camilo Santa, Juan Pablo Marulanda","submitted_at":"2020-04-02T14:47:37Z","abstract_excerpt":"Detection of gravitational waves (GW) from compact binary mergers provide a new window into multi-messenger astrophysics. The standard technique to determine the merger parameters is matched filtering, consisting in comparing the signal to a template bank. This approach can be time consuming and computationally expensive due to the large amount of experimental data which needs to be analyzed.\n  In the attempt to find more efficient data analysis methods we develop a new frequency domain convolutional neural network (FCNN) to predict the merger masses from the spectrogram of the detector signal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01050","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/2004.01050/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":"2004.01050","created_at":"2026-07-05T01:46:17.615276+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.01050v2","created_at":"2026-07-05T01:46:17.615276+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01050","created_at":"2026-07-05T01:46:17.615276+00:00"},{"alias_kind":"pith_short_12","alias_value":"WYF4552RYVVJ","created_at":"2026-07-05T01:46:17.615276+00:00"},{"alias_kind":"pith_short_16","alias_value":"WYF4552RYVVJXLWT","created_at":"2026-07-05T01:46:17.615276+00:00"},{"alias_kind":"pith_short_8","alias_value":"WYF4552R","created_at":"2026-07-05T01:46:17.615276+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/WYF4552RYVVJXLWTYYQHG3Y465","json":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465.json","graph_json":"https://pith.science/api/pith-number/WYF4552RYVVJXLWTYYQHG3Y465/graph.json","events_json":"https://pith.science/api/pith-number/WYF4552RYVVJXLWTYYQHG3Y465/events.json","paper":"https://pith.science/paper/WYF4552R"},"agent_actions":{"view_html":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465","download_json":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465.json","view_paper":"https://pith.science/paper/WYF4552R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.01050&json=true","fetch_graph":"https://pith.science/api/pith-number/WYF4552RYVVJXLWTYYQHG3Y465/graph.json","fetch_events":"https://pith.science/api/pith-number/WYF4552RYVVJXLWTYYQHG3Y465/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465/action/storage_attestation","attest_author":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465/action/author_attestation","sign_citation":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465/action/citation_signature","submit_replication":"https://pith.science/pith/WYF4552RYVVJXLWTYYQHG3Y465/action/replication_record"}},"created_at":"2026-07-05T01:46:17.615276+00:00","updated_at":"2026-07-05T01:46:17.615276+00:00"}