{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YXYEFH4JPUMF33PNHNO7BLF6HM","short_pith_number":"pith:YXYEFH4J","schema_version":"1.0","canonical_sha256":"c5f0429f897d185deded3b5df0acbe3b038419076fbeb70e80ad3b9156a4cc2b","source":{"kind":"arxiv","id":"1904.11951","version":1},"attestation_state":"computed","paper":{"title":"Optical Frequency Comb Noise Characterization Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.optics","quant-ph"],"primary_cat":"eess.SP","authors_text":"Darko Zibar, Giovanni Brajato, Lars Lundberg, Victor Torres-Company","submitted_at":"2019-04-23T07:36:44Z","abstract_excerpt":"A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods."},"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":"1904.11951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-04-23T07:36:44Z","cross_cats_sorted":["physics.optics","quant-ph"],"title_canon_sha256":"15575d3d3ebe0c70de7bec5cef889b183f58bb3761e3811c4b18f8a114903cab","abstract_canon_sha256":"925e2d690b525f5496980ce7657d5761b2341ef251947f26fa724942b214b5de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:40.535252Z","signature_b64":"lyuwVlV860ny/pSpRavMzgzhGJsDUn5ex5/pt+xcH+2qHfoQMAlYfjhUdlv9VkGxNsFb84efmEagDmNvQsNiDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5f0429f897d185deded3b5df0acbe3b038419076fbeb70e80ad3b9156a4cc2b","last_reissued_at":"2026-05-17T23:47:40.534613Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:40.534613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optical Frequency Comb Noise Characterization Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.optics","quant-ph"],"primary_cat":"eess.SP","authors_text":"Darko Zibar, Giovanni Brajato, Lars Lundberg, Victor Torres-Company","submitted_at":"2019-04-23T07:36:44Z","abstract_excerpt":"A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11951","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":"1904.11951","created_at":"2026-05-17T23:47:40.534698+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.11951v1","created_at":"2026-05-17T23:47:40.534698+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11951","created_at":"2026-05-17T23:47:40.534698+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXYEFH4JPUMF","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXYEFH4JPUMF33PN","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXYEFH4J","created_at":"2026-05-18T12:33:33.725879+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/YXYEFH4JPUMF33PNHNO7BLF6HM","json":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM.json","graph_json":"https://pith.science/api/pith-number/YXYEFH4JPUMF33PNHNO7BLF6HM/graph.json","events_json":"https://pith.science/api/pith-number/YXYEFH4JPUMF33PNHNO7BLF6HM/events.json","paper":"https://pith.science/paper/YXYEFH4J"},"agent_actions":{"view_html":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM","download_json":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM.json","view_paper":"https://pith.science/paper/YXYEFH4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.11951&json=true","fetch_graph":"https://pith.science/api/pith-number/YXYEFH4JPUMF33PNHNO7BLF6HM/graph.json","fetch_events":"https://pith.science/api/pith-number/YXYEFH4JPUMF33PNHNO7BLF6HM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM/action/storage_attestation","attest_author":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM/action/author_attestation","sign_citation":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM/action/citation_signature","submit_replication":"https://pith.science/pith/YXYEFH4JPUMF33PNHNO7BLF6HM/action/replication_record"}},"created_at":"2026-05-17T23:47:40.534698+00:00","updated_at":"2026-05-17T23:47:40.534698+00:00"}