{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CTZZQUSLOSS7OTAHK5XCQMUKMT","short_pith_number":"pith:CTZZQUSL","schema_version":"1.0","canonical_sha256":"14f398524b74a5f74c07576e28328a64e10ec3a9e9739bfcde9d37e9e1f4ed39","source":{"kind":"arxiv","id":"2505.10636","version":2},"attestation_state":"computed","paper":{"title":"Searching optimal scales for reconstructing cosmological initial conditions using convolutional neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Atsushi J. Nishizawa, Kenji Hasegawa, Kiyotomo Ichiki, Koichiro Nakashima","submitted_at":"2025-05-15T18:17:32Z","abstract_excerpt":"Reconstructing the initial density field of the Universe from the late-time matter distribution is a nontrivial task with implications for understanding structure formation in cosmology, offering insights into early Universe conditions. Convolutional neural networks (CNNs) have shown promise in tackling this problem by learning the complex mapping from nonlinear evolved fields back to initial conditions. Here we investigate the effect of varying input sub-box size in single-input CNNs. We find that intermediate scales ($L_\\mathrm{sub} \\sim 152\\,h^{-1}\\,\\mathrm{Mpc}$) strike the best balance be"},"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":"2505.10636","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.CO","submitted_at":"2025-05-15T18:17:32Z","cross_cats_sorted":[],"title_canon_sha256":"83584093249aeaf83156f53aa075153cbc2cff75317447d57d4b7711fbd4e7ad","abstract_canon_sha256":"d51bff3662ac738723eb4af1473aef5b2b58ecb9660375b907c979d0549e65e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T01:18:17.030848Z","signature_b64":"YNt09x7nKggf1PwIEMpnwgurGs/L732xK5obWprT95YD0CyS5scmQNXFg4M1TR2n8r9fmrK64Xg0CMSQUg7rCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14f398524b74a5f74c07576e28328a64e10ec3a9e9739bfcde9d37e9e1f4ed39","last_reissued_at":"2026-07-20T01:18:17.029845Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T01:18:17.029845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Searching optimal scales for reconstructing cosmological initial conditions using convolutional neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Atsushi J. Nishizawa, Kenji Hasegawa, Kiyotomo Ichiki, Koichiro Nakashima","submitted_at":"2025-05-15T18:17:32Z","abstract_excerpt":"Reconstructing the initial density field of the Universe from the late-time matter distribution is a nontrivial task with implications for understanding structure formation in cosmology, offering insights into early Universe conditions. Convolutional neural networks (CNNs) have shown promise in tackling this problem by learning the complex mapping from nonlinear evolved fields back to initial conditions. Here we investigate the effect of varying input sub-box size in single-input CNNs. We find that intermediate scales ($L_\\mathrm{sub} \\sim 152\\,h^{-1}\\,\\mathrm{Mpc}$) strike the best balance be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10636","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/2505.10636/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":"2505.10636","created_at":"2026-07-20T01:18:17.030323+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10636v2","created_at":"2026-07-20T01:18:17.030323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10636","created_at":"2026-07-20T01:18:17.030323+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTZZQUSLOSS7","created_at":"2026-07-20T01:18:17.030323+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTZZQUSLOSS7OTAH","created_at":"2026-07-20T01:18:17.030323+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTZZQUSL","created_at":"2026-07-20T01:18:17.030323+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/CTZZQUSLOSS7OTAHK5XCQMUKMT","json":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT.json","graph_json":"https://pith.science/api/pith-number/CTZZQUSLOSS7OTAHK5XCQMUKMT/graph.json","events_json":"https://pith.science/api/pith-number/CTZZQUSLOSS7OTAHK5XCQMUKMT/events.json","paper":"https://pith.science/paper/CTZZQUSL"},"agent_actions":{"view_html":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT","download_json":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT.json","view_paper":"https://pith.science/paper/CTZZQUSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10636&json=true","fetch_graph":"https://pith.science/api/pith-number/CTZZQUSLOSS7OTAHK5XCQMUKMT/graph.json","fetch_events":"https://pith.science/api/pith-number/CTZZQUSLOSS7OTAHK5XCQMUKMT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT/action/storage_attestation","attest_author":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT/action/author_attestation","sign_citation":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT/action/citation_signature","submit_replication":"https://pith.science/pith/CTZZQUSLOSS7OTAHK5XCQMUKMT/action/replication_record"}},"created_at":"2026-07-20T01:18:17.030323+00:00","updated_at":"2026-07-20T01:18:17.030323+00:00"}