{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5MR2GRVUXR3ZIWZNR33SV75AIZ","short_pith_number":"pith:5MR2GRVU","schema_version":"1.0","canonical_sha256":"eb23a346b4bc77945b2d8ef72affa04675a474bab33c32b85f450c2e09de6c1d","source":{"kind":"arxiv","id":"2303.17939","version":1},"attestation_state":"computed","paper":{"title":"LyAl-Net: A high-efficiency Lyman-$\\alpha$ forest simulation with a neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"astro-ph.CO","authors_text":"Chotipan Boonkongkird, Eleni Tsaprazi, Guilhem Lavaux, Natalia Porqueres, Sebastien Peirani, Yohan Dubois","submitted_at":"2023-03-31T10:06:59Z","abstract_excerpt":"The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours for a modest coverage in cosmological scales ($\\approx (100 {h^{-1}}\\,\\text{Mpc})^3)$). The possibility to generate large quantities of mock Lyman-$\\alpha$ observations opens up the possibility of much better control on covariance matrices estimate for cosmological parameters inference, and on the impact of systematics due to baryonic effects. We present a machine learning approach to emulate the hydrodynamical simu"},"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":"2303.17939","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2023-03-31T10:06:59Z","cross_cats_sorted":["physics.data-an"],"title_canon_sha256":"e4c27bc8d610807f9eae61d9f5396e4df22a4cbef0d1461c74df734f6effdbc8","abstract_canon_sha256":"5cf777c0c49997c2b621f46f527d61403ae55392290452c3f7eed8b66cf487d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:48.006967Z","signature_b64":"+cVROiGkMOn0rzWgUhb5TKIEJWNPwwLB1fN3ge0WwNpVjQf8nA18bkuPtM1uYuEqKM7RctRjYufNDJhCb/YFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb23a346b4bc77945b2d8ef72affa04675a474bab33c32b85f450c2e09de6c1d","last_reissued_at":"2026-07-05T05:56:48.006418Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:48.006418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LyAl-Net: A high-efficiency Lyman-$\\alpha$ forest simulation with a neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"astro-ph.CO","authors_text":"Chotipan Boonkongkird, Eleni Tsaprazi, Guilhem Lavaux, Natalia Porqueres, Sebastien Peirani, Yohan Dubois","submitted_at":"2023-03-31T10:06:59Z","abstract_excerpt":"The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours for a modest coverage in cosmological scales ($\\approx (100 {h^{-1}}\\,\\text{Mpc})^3)$). The possibility to generate large quantities of mock Lyman-$\\alpha$ observations opens up the possibility of much better control on covariance matrices estimate for cosmological parameters inference, and on the impact of systematics due to baryonic effects. We present a machine learning approach to emulate the hydrodynamical simu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17939","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/2303.17939/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":"2303.17939","created_at":"2026-07-05T05:56:48.006482+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.17939v1","created_at":"2026-07-05T05:56:48.006482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17939","created_at":"2026-07-05T05:56:48.006482+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MR2GRVUXR3Z","created_at":"2026-07-05T05:56:48.006482+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MR2GRVUXR3ZIWZN","created_at":"2026-07-05T05:56:48.006482+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MR2GRVU","created_at":"2026-07-05T05:56:48.006482+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03753","citing_title":"The impact of source and survey modelling on the connection between [O III] emitters and Ly $\\alpha$ forest transmission at z ~ 6","ref_index":76,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22489","citing_title":"Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\\alpha$ forest","ref_index":251,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ","json":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ.json","graph_json":"https://pith.science/api/pith-number/5MR2GRVUXR3ZIWZNR33SV75AIZ/graph.json","events_json":"https://pith.science/api/pith-number/5MR2GRVUXR3ZIWZNR33SV75AIZ/events.json","paper":"https://pith.science/paper/5MR2GRVU"},"agent_actions":{"view_html":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ","download_json":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ.json","view_paper":"https://pith.science/paper/5MR2GRVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.17939&json=true","fetch_graph":"https://pith.science/api/pith-number/5MR2GRVUXR3ZIWZNR33SV75AIZ/graph.json","fetch_events":"https://pith.science/api/pith-number/5MR2GRVUXR3ZIWZNR33SV75AIZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ/action/storage_attestation","attest_author":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ/action/author_attestation","sign_citation":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ/action/citation_signature","submit_replication":"https://pith.science/pith/5MR2GRVUXR3ZIWZNR33SV75AIZ/action/replication_record"}},"created_at":"2026-07-05T05:56:48.006482+00:00","updated_at":"2026-07-05T05:56:48.006482+00:00"}