{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PJYX73BNUEBZ6VIQXWM2GC34RK","short_pith_number":"pith:PJYX73BN","schema_version":"1.0","canonical_sha256":"7a717fec2da1039f5510bd99a30b7c8a870299df94c76bc2e2d9a5c891542dab","source":{"kind":"arxiv","id":"2605.31579","version":1},"attestation_state":"computed","paper":{"title":"Functional Multi-Target Detection via Bispectrum Inversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.ST","stat.TH"],"primary_cat":"eess.SP","authors_text":"Anna Little, Daniel Sanz-Alonso, Mikhail Sweeney, Ruiyi Yang","submitted_at":"2026-05-29T17:47:45Z","abstract_excerpt":"This paper develops a functional theory for multi-target detection, where a compactly supported signal is recovered from a single noisy observation containing many unknown translations of the signal. Our formulation allows continuous, off-grid translations and correlated stationary Gaussian process noise, extending beyond the discrete, grid-aligned, white-noise models common in prior work. We analyze two uninitialized recovery algorithms based on autocorrelation analysis; in particular, both algorithms first estimate the signal's bispectrum via a debiased third-order empirical autocorrelation."},"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":"2605.31579","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-05-29T17:47:45Z","cross_cats_sorted":["cs.IT","math.IT","math.ST","stat.TH"],"title_canon_sha256":"ca0665fe154045a2534298d2cebb8899267ffd84de930605d411e2f556cbcf27","abstract_canon_sha256":"adf68e16c151161e078f5ccca2bda228f440aadb169f2781a13a9f29767f3ca5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T02:04:14.234998Z","signature_b64":"xAbBbf1gGMjxvjCw5kuTCFDnTao8+Pzuu94SgNvLZ9Fh20+mcT5Hh6383aP9gxpNPGljN2XCmLfYtVmOVkHTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a717fec2da1039f5510bd99a30b7c8a870299df94c76bc2e2d9a5c891542dab","last_reissued_at":"2026-06-01T02:04:14.234169Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T02:04:14.234169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Functional Multi-Target Detection via Bispectrum Inversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.ST","stat.TH"],"primary_cat":"eess.SP","authors_text":"Anna Little, Daniel Sanz-Alonso, Mikhail Sweeney, Ruiyi Yang","submitted_at":"2026-05-29T17:47:45Z","abstract_excerpt":"This paper develops a functional theory for multi-target detection, where a compactly supported signal is recovered from a single noisy observation containing many unknown translations of the signal. Our formulation allows continuous, off-grid translations and correlated stationary Gaussian process noise, extending beyond the discrete, grid-aligned, white-noise models common in prior work. We analyze two uninitialized recovery algorithms based on autocorrelation analysis; in particular, both algorithms first estimate the signal's bispectrum via a debiased third-order empirical autocorrelation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31579","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/2605.31579/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":"2605.31579","created_at":"2026-06-01T02:04:14.234296+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31579v1","created_at":"2026-06-01T02:04:14.234296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31579","created_at":"2026-06-01T02:04:14.234296+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJYX73BNUEBZ","created_at":"2026-06-01T02:04:14.234296+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJYX73BNUEBZ6VIQ","created_at":"2026-06-01T02:04:14.234296+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJYX73BN","created_at":"2026-06-01T02:04:14.234296+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/PJYX73BNUEBZ6VIQXWM2GC34RK","json":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK.json","graph_json":"https://pith.science/api/pith-number/PJYX73BNUEBZ6VIQXWM2GC34RK/graph.json","events_json":"https://pith.science/api/pith-number/PJYX73BNUEBZ6VIQXWM2GC34RK/events.json","paper":"https://pith.science/paper/PJYX73BN"},"agent_actions":{"view_html":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK","download_json":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK.json","view_paper":"https://pith.science/paper/PJYX73BN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31579&json=true","fetch_graph":"https://pith.science/api/pith-number/PJYX73BNUEBZ6VIQXWM2GC34RK/graph.json","fetch_events":"https://pith.science/api/pith-number/PJYX73BNUEBZ6VIQXWM2GC34RK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK/action/storage_attestation","attest_author":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK/action/author_attestation","sign_citation":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK/action/citation_signature","submit_replication":"https://pith.science/pith/PJYX73BNUEBZ6VIQXWM2GC34RK/action/replication_record"}},"created_at":"2026-06-01T02:04:14.234296+00:00","updated_at":"2026-06-01T02:04:14.234296+00:00"}