{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MSCLEP3BVDO5IIWB6JMM4KPC4O","short_pith_number":"pith:MSCLEP3B","schema_version":"1.0","canonical_sha256":"6484b23f61a8ddd422c1f258ce29e2e3ab759f5a9d3648ea76faec61b5b08aa0","source":{"kind":"arxiv","id":"2406.14101","version":1},"attestation_state":"computed","paper":{"title":"Peculiar Velocity Reconstruction From Simulations and Observations Using Deep Learning Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Xiaohu Yang, Yuyu Wang","submitted_at":"2024-06-20T08:34:37Z","abstract_excerpt":"In this paper, we introduce a Unet model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the Unet model with simulation data under more realistic conditions, including the redshift space distortion (RSD) effect and halo mass threshold. Our results show that the Unet model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient and 27% in average coherence. The deep le"},"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":"2406.14101","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-06-20T08:34:37Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"485da948f1c4f172241e37a3e3b391bb47439aadbd815f0a59aed00531360139","abstract_canon_sha256":"a83d67a1c662c8ca169791c41837b61474af78a4e4294bd517541fd920bdf0bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:38.896892Z","signature_b64":"EzcN0Tci9cDk9buTq1B+XUO5VYHkvsj1aWhalSM+0qnZNH+rjMwCjBZC56HC78qejQtTutboVIP5FNDKKrJnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6484b23f61a8ddd422c1f258ce29e2e3ab759f5a9d3648ea76faec61b5b08aa0","last_reissued_at":"2026-07-05T08:34:38.896481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:38.896481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Peculiar Velocity Reconstruction From Simulations and Observations Using Deep Learning Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Xiaohu Yang, Yuyu Wang","submitted_at":"2024-06-20T08:34:37Z","abstract_excerpt":"In this paper, we introduce a Unet model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the Unet model with simulation data under more realistic conditions, including the redshift space distortion (RSD) effect and halo mass threshold. Our results show that the Unet model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient and 27% in average coherence. The deep le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.14101","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/2406.14101/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":"2406.14101","created_at":"2026-07-05T08:34:38.896537+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.14101v1","created_at":"2026-07-05T08:34:38.896537+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.14101","created_at":"2026-07-05T08:34:38.896537+00:00"},{"alias_kind":"pith_short_12","alias_value":"MSCLEP3BVDO5","created_at":"2026-07-05T08:34:38.896537+00:00"},{"alias_kind":"pith_short_16","alias_value":"MSCLEP3BVDO5IIWB","created_at":"2026-07-05T08:34:38.896537+00:00"},{"alias_kind":"pith_short_8","alias_value":"MSCLEP3B","created_at":"2026-07-05T08:34:38.896537+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05047","citing_title":"Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21483","citing_title":"Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction","ref_index":15,"is_internal_anchor":false},{"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":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O","json":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O.json","graph_json":"https://pith.science/api/pith-number/MSCLEP3BVDO5IIWB6JMM4KPC4O/graph.json","events_json":"https://pith.science/api/pith-number/MSCLEP3BVDO5IIWB6JMM4KPC4O/events.json","paper":"https://pith.science/paper/MSCLEP3B"},"agent_actions":{"view_html":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O","download_json":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O.json","view_paper":"https://pith.science/paper/MSCLEP3B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.14101&json=true","fetch_graph":"https://pith.science/api/pith-number/MSCLEP3BVDO5IIWB6JMM4KPC4O/graph.json","fetch_events":"https://pith.science/api/pith-number/MSCLEP3BVDO5IIWB6JMM4KPC4O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O/action/storage_attestation","attest_author":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O/action/author_attestation","sign_citation":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O/action/citation_signature","submit_replication":"https://pith.science/pith/MSCLEP3BVDO5IIWB6JMM4KPC4O/action/replication_record"}},"created_at":"2026-07-05T08:34:38.896537+00:00","updated_at":"2026-07-05T08:34:38.896537+00:00"}