{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IL6PRO75EW7J5SP3IDDN2WLWTM","short_pith_number":"pith:IL6PRO75","schema_version":"1.0","canonical_sha256":"42fcf8bbfd25be9ec9fb40c6dd59769b009913c763c4cb27448574a26d959e9e","source":{"kind":"arxiv","id":"2507.00135","version":1},"attestation_state":"computed","paper":{"title":"DeepCHART: Mapping the 3D dark matter density field from Ly$\\alpha$ forest surveys using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"Girish Kulkarni, Matteo Viel, Soumak Maitra (TIFR)","submitted_at":"2025-06-30T18:00:06Z","abstract_excerpt":"We present DeepCHART (Deep learning for Cosmological Heterogeneity and Astrophysical Reconstruction via Tomography), a deep learning framework designed to reconstruct the three-dimensional dark matter density field at redshift $z=2.5$ from Ly$\\alpha$ forest spectra. Leveraging a 3D variational autoencoder with a U-Net architecture, DeepCHART performs fast, likelihood-free inference, accurately capturing the non-linear gravitational dynamics and baryonic processes embedded in cosmological hydrodynamical simulations. When applied to joint datasets combining Ly$\\alpha$ forest absorption and coeva"},"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":"2507.00135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2025-06-30T18:00:06Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"5c17bb46d0edbc5b40097a4560331cd02326d104e964fac24adfa309ff14def2","abstract_canon_sha256":"29e04ca994a2591fa1d615ecc1196fefab020f1b3234a33a8db2d16dd2200ed6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T14:05:45.432080Z","signature_b64":"wbkzdhSe6OeKqA3P8exlHDJA8D8GcIvBXDxDQ8fq20UR9wX4U1FpOkHOPcByPBuT2P1Rsm0CD99q5P5rfLRNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42fcf8bbfd25be9ec9fb40c6dd59769b009913c763c4cb27448574a26d959e9e","last_reissued_at":"2026-06-03T14:05:45.431540Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T14:05:45.431540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepCHART: Mapping the 3D dark matter density field from Ly$\\alpha$ forest surveys using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"Girish Kulkarni, Matteo Viel, Soumak Maitra (TIFR)","submitted_at":"2025-06-30T18:00:06Z","abstract_excerpt":"We present DeepCHART (Deep learning for Cosmological Heterogeneity and Astrophysical Reconstruction via Tomography), a deep learning framework designed to reconstruct the three-dimensional dark matter density field at redshift $z=2.5$ from Ly$\\alpha$ forest spectra. Leveraging a 3D variational autoencoder with a U-Net architecture, DeepCHART performs fast, likelihood-free inference, accurately capturing the non-linear gravitational dynamics and baryonic processes embedded in cosmological hydrodynamical simulations. When applied to joint datasets combining Ly$\\alpha$ forest absorption and coeva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00135","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/2507.00135/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":"2507.00135","created_at":"2026-06-03T14:05:45.431605+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.00135v1","created_at":"2026-06-03T14:05:45.431605+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00135","created_at":"2026-06-03T14:05:45.431605+00:00"},{"alias_kind":"pith_short_12","alias_value":"IL6PRO75EW7J","created_at":"2026-06-03T14:05:45.431605+00:00"},{"alias_kind":"pith_short_16","alias_value":"IL6PRO75EW7J5SP3","created_at":"2026-06-03T14:05:45.431605+00:00"},{"alias_kind":"pith_short_8","alias_value":"IL6PRO75","created_at":"2026-06-03T14:05:45.431605+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/IL6PRO75EW7J5SP3IDDN2WLWTM","json":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM.json","graph_json":"https://pith.science/api/pith-number/IL6PRO75EW7J5SP3IDDN2WLWTM/graph.json","events_json":"https://pith.science/api/pith-number/IL6PRO75EW7J5SP3IDDN2WLWTM/events.json","paper":"https://pith.science/paper/IL6PRO75"},"agent_actions":{"view_html":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM","download_json":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM.json","view_paper":"https://pith.science/paper/IL6PRO75","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.00135&json=true","fetch_graph":"https://pith.science/api/pith-number/IL6PRO75EW7J5SP3IDDN2WLWTM/graph.json","fetch_events":"https://pith.science/api/pith-number/IL6PRO75EW7J5SP3IDDN2WLWTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM/action/storage_attestation","attest_author":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM/action/author_attestation","sign_citation":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM/action/citation_signature","submit_replication":"https://pith.science/pith/IL6PRO75EW7J5SP3IDDN2WLWTM/action/replication_record"}},"created_at":"2026-06-03T14:05:45.431605+00:00","updated_at":"2026-06-03T14:05:45.431605+00:00"}