{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KTYD36FTYWNSS264NYSSIGQAFN","short_pith_number":"pith:KTYD36FT","schema_version":"1.0","canonical_sha256":"54f03df8b3c59b296bdc6e25241a002b7cdf4e5bb0ebbacab95d451ab62dd5fa","source":{"kind":"arxiv","id":"2411.17853","version":1},"attestation_state":"computed","paper":{"title":"Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Ander Artola, Benedetta Spina, Emanuele P. Farina, Ewald Puchwein, Fahad Nasir, Frederick B. Davies, Klaudia Protu\\v{s}ov\\'a, Prakash Gaikwad, Sarah E. I. Bosman","submitted_at":"2024-11-26T20:09:31Z","abstract_excerpt":"We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density field. We design and train a Bayesian neural network on the supervised regression task of recovering the optical depth-weighted density field $\\Delta_\\tau$ as well as its reconstruction uncertainty from the Lyman-alpha forest flux field. W"},"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":"2411.17853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2024-11-26T20:09:31Z","cross_cats_sorted":[],"title_canon_sha256":"0faa1fab996eb9cfb2db8376d31bd5223a6d928b804cffefb1f220f2ebe26335","abstract_canon_sha256":"09d51bfffeecaf2519bba0f4ab196abb22a94a8a9f42f6908898399de22d529d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:07.945583Z","signature_b64":"hePjtgkhxHq/VWNaMDQ500ARYbsCdxAwXSFDVmiQmQf4W48tU8gtF3FfZHUxcc1HaiQFqysjZetMvAcg+TfjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54f03df8b3c59b296bdc6e25241a002b7cdf4e5bb0ebbacab95d451ab62dd5fa","last_reissued_at":"2026-07-05T09:41:07.945144Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:07.945144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Ander Artola, Benedetta Spina, Emanuele P. Farina, Ewald Puchwein, Fahad Nasir, Frederick B. Davies, Klaudia Protu\\v{s}ov\\'a, Prakash Gaikwad, Sarah E. I. Bosman","submitted_at":"2024-11-26T20:09:31Z","abstract_excerpt":"We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density field. We design and train a Bayesian neural network on the supervised regression task of recovering the optical depth-weighted density field $\\Delta_\\tau$ as well as its reconstruction uncertainty from the Lyman-alpha forest flux field. W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17853","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/2411.17853/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":"2411.17853","created_at":"2026-07-05T09:41:07.945199+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.17853v1","created_at":"2026-07-05T09:41:07.945199+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17853","created_at":"2026-07-05T09:41:07.945199+00:00"},{"alias_kind":"pith_short_12","alias_value":"KTYD36FTYWNS","created_at":"2026-07-05T09:41:07.945199+00:00"},{"alias_kind":"pith_short_16","alias_value":"KTYD36FTYWNSS264","created_at":"2026-07-05T09:41:07.945199+00:00"},{"alias_kind":"pith_short_8","alias_value":"KTYD36FT","created_at":"2026-07-05T09:41:07.945199+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.19938","citing_title":"Ringing of the Reionization: A first direct measurement of the intergalactic pressure smoothing scale at redshift z>4.2 as imprinted onto small-scale peculiar velocities in the Lyman-alpha forest","ref_index":175,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN","json":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN.json","graph_json":"https://pith.science/api/pith-number/KTYD36FTYWNSS264NYSSIGQAFN/graph.json","events_json":"https://pith.science/api/pith-number/KTYD36FTYWNSS264NYSSIGQAFN/events.json","paper":"https://pith.science/paper/KTYD36FT"},"agent_actions":{"view_html":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN","download_json":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN.json","view_paper":"https://pith.science/paper/KTYD36FT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.17853&json=true","fetch_graph":"https://pith.science/api/pith-number/KTYD36FTYWNSS264NYSSIGQAFN/graph.json","fetch_events":"https://pith.science/api/pith-number/KTYD36FTYWNSS264NYSSIGQAFN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN/action/storage_attestation","attest_author":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN/action/author_attestation","sign_citation":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN/action/citation_signature","submit_replication":"https://pith.science/pith/KTYD36FTYWNSS264NYSSIGQAFN/action/replication_record"}},"created_at":"2026-07-05T09:41:07.945199+00:00","updated_at":"2026-07-05T09:41:07.945199+00:00"}