{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:1994:EITOI4K33FBK6SAJCX5ZPSB2NT","short_pith_number":"pith:EITOI4K3","schema_version":"1.0","canonical_sha256":"2226e4715bd942af480915fb97c83a6ce318f1072fb2fc7190a275157e102c98","source":{"kind":"arxiv","id":"astro-ph/9410080","version":1},"attestation_state":"computed","paper":{"title":"Wiener Reconstruction of The Large Scale Structure","license":"","headline":"","cross_cats":[],"primary_cat":"astro-ph","authors_text":"K.B. Fisher, O. Lahav, S. Zaroubi, Y. Hoffman","submitted_at":"1994-10-25T07:44:02Z","abstract_excerpt":"The formalism of Wiener filtering is developed here for the purpose of reconstructing the large scale structure of the universe from noisy, sparse and incomplete data. The method is based on a linear minimum variance solution, given data and an assumed \\prior model which specifies the covariance matrix of the field to be reconstructed. While earlier applications of the Wiener filter have focused on estimation, namely suppressing the noise in the measured quantities, we extend the method here to perform both prediction and dynamical reconstruction. The Wiener filter is used to predict the value"},"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":"astro-ph/9410080","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"astro-ph","submitted_at":"1994-10-25T07:44:02Z","cross_cats_sorted":[],"title_canon_sha256":"a2ddff5696292f95e35104b34c421aeb29212f805852c14197890555b8984f4f","abstract_canon_sha256":"2e42abb8a2fd1bd431364e5e962ba287e1b45e4b731139cd1aa80acd76a78bfc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T15:56:09.959505Z","signature_b64":"kM5e+PT4WMgAN0yLujqF40WWghoiJ1DHrwhT3VLMFYbXZ/vsjwD7XIIrVzff34ZNt0gdMD4Eg0SLYNk0+fITDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2226e4715bd942af480915fb97c83a6ce318f1072fb2fc7190a275157e102c98","last_reissued_at":"2026-07-04T15:56:09.958988Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T15:56:09.958988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wiener Reconstruction of The Large Scale Structure","license":"","headline":"","cross_cats":[],"primary_cat":"astro-ph","authors_text":"K.B. Fisher, O. Lahav, S. Zaroubi, Y. Hoffman","submitted_at":"1994-10-25T07:44:02Z","abstract_excerpt":"The formalism of Wiener filtering is developed here for the purpose of reconstructing the large scale structure of the universe from noisy, sparse and incomplete data. The method is based on a linear minimum variance solution, given data and an assumed \\prior model which specifies the covariance matrix of the field to be reconstructed. While earlier applications of the Wiener filter have focused on estimation, namely suppressing the noise in the measured quantities, we extend the method here to perform both prediction and dynamical reconstruction. The Wiener filter is used to predict the value"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"astro-ph/9410080","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/astro-ph/9410080/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":"astro-ph/9410080","created_at":"2026-07-04T15:56:09.959048+00:00"},{"alias_kind":"arxiv_version","alias_value":"astro-ph/9410080v1","created_at":"2026-07-04T15:56:09.959048+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.astro-ph/9410080","created_at":"2026-07-04T15:56:09.959048+00:00"},{"alias_kind":"pith_short_12","alias_value":"EITOI4K33FBK","created_at":"2026-07-04T15:56:09.959048+00:00"},{"alias_kind":"pith_short_16","alias_value":"EITOI4K33FBK6SAJ","created_at":"2026-07-04T15:56:09.959048+00:00"},{"alias_kind":"pith_short_8","alias_value":"EITOI4K3","created_at":"2026-07-04T15:56:09.959048+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.21483","citing_title":"Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2604.14653","citing_title":"Closing the Observational Gap in Cosmic Dynamics: AI-Enabled Reconstruction of the Universe's Vorticity and Rotational Flow Morphology","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT","json":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT.json","graph_json":"https://pith.science/api/pith-number/EITOI4K33FBK6SAJCX5ZPSB2NT/graph.json","events_json":"https://pith.science/api/pith-number/EITOI4K33FBK6SAJCX5ZPSB2NT/events.json","paper":"https://pith.science/paper/EITOI4K3"},"agent_actions":{"view_html":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT","download_json":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT.json","view_paper":"https://pith.science/paper/EITOI4K3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=astro-ph/9410080&json=true","fetch_graph":"https://pith.science/api/pith-number/EITOI4K33FBK6SAJCX5ZPSB2NT/graph.json","fetch_events":"https://pith.science/api/pith-number/EITOI4K33FBK6SAJCX5ZPSB2NT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT/action/storage_attestation","attest_author":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT/action/author_attestation","sign_citation":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT/action/citation_signature","submit_replication":"https://pith.science/pith/EITOI4K33FBK6SAJCX5ZPSB2NT/action/replication_record"}},"created_at":"2026-07-04T15:56:09.959048+00:00","updated_at":"2026-07-04T15:56:09.959048+00:00"}