{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YQZE6XVSWRNCY27YFVQ3FYQRZW","short_pith_number":"pith:YQZE6XVS","schema_version":"1.0","canonical_sha256":"c4324f5eb2b45a2c6bf82d61b2e211cd8cacbff661379a04b00e320e62d012e4","source":{"kind":"arxiv","id":"2508.12032","version":1},"attestation_state":"computed","paper":{"title":"Cosmology-informed Neural Networks to infer dark energy equation-of-state","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Anshul Verma, David F. Mota, Pavan K. Aluri, Shashwat Sourav","submitted_at":"2025-08-16T12:59:30Z","abstract_excerpt":"We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pantheon+ Type Ia supernova compilation. First, we train a physics-informed neural network to learn the solution of the Friedmann equation and accurately reproduce the matter density term x_m(z) = Omega_m,0 (1+z)^3 across a range of Omega_m,0. For each of five two-parameter equation-of-state (EoS) forms: Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), Jassal-Bagla-Padmanabhan (JBP), Linear-z, and Logarithmic-z, "},"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":"2508.12032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2025-08-16T12:59:30Z","cross_cats_sorted":[],"title_canon_sha256":"a1a9bcd23710460abd98bad9fb4c3b28d3519a8d5377957adf42ceaf319c986d","abstract_canon_sha256":"502a460819b08028d0770691e13cf7c140f39348e2fa6a5413aa799aca57ae36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:59.019821Z","signature_b64":"2BU7j650P/Gfp91wU4/v2nCcFbNu2AGC5fsfY+rLDE6gS3H0suw2PeeGWoCaX+CzOa7OSoHl1RKVpAEACPx9BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4324f5eb2b45a2c6bf82d61b2e211cd8cacbff661379a04b00e320e62d012e4","last_reissued_at":"2026-07-05T11:54:59.019410Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:59.019410Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cosmology-informed Neural Networks to infer dark energy equation-of-state","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Anshul Verma, David F. Mota, Pavan K. Aluri, Shashwat Sourav","submitted_at":"2025-08-16T12:59:30Z","abstract_excerpt":"We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pantheon+ Type Ia supernova compilation. First, we train a physics-informed neural network to learn the solution of the Friedmann equation and accurately reproduce the matter density term x_m(z) = Omega_m,0 (1+z)^3 across a range of Omega_m,0. For each of five two-parameter equation-of-state (EoS) forms: Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), Jassal-Bagla-Padmanabhan (JBP), Linear-z, and Logarithmic-z, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.12032","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/2508.12032/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":"2508.12032","created_at":"2026-07-05T11:54:59.019465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.12032v1","created_at":"2026-07-05T11:54:59.019465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.12032","created_at":"2026-07-05T11:54:59.019465+00:00"},{"alias_kind":"pith_short_12","alias_value":"YQZE6XVSWRNC","created_at":"2026-07-05T11:54:59.019465+00:00"},{"alias_kind":"pith_short_16","alias_value":"YQZE6XVSWRNCY27Y","created_at":"2026-07-05T11:54:59.019465+00:00"},{"alias_kind":"pith_short_8","alias_value":"YQZE6XVS","created_at":"2026-07-05T11:54:59.019465+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.30139","citing_title":"Cosmo-PINN: A Physics-Informed Neural Network for Cosmological Reconstruction","ref_index":93,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW","json":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW.json","graph_json":"https://pith.science/api/pith-number/YQZE6XVSWRNCY27YFVQ3FYQRZW/graph.json","events_json":"https://pith.science/api/pith-number/YQZE6XVSWRNCY27YFVQ3FYQRZW/events.json","paper":"https://pith.science/paper/YQZE6XVS"},"agent_actions":{"view_html":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW","download_json":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW.json","view_paper":"https://pith.science/paper/YQZE6XVS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.12032&json=true","fetch_graph":"https://pith.science/api/pith-number/YQZE6XVSWRNCY27YFVQ3FYQRZW/graph.json","fetch_events":"https://pith.science/api/pith-number/YQZE6XVSWRNCY27YFVQ3FYQRZW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW/action/storage_attestation","attest_author":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW/action/author_attestation","sign_citation":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW/action/citation_signature","submit_replication":"https://pith.science/pith/YQZE6XVSWRNCY27YFVQ3FYQRZW/action/replication_record"}},"created_at":"2026-07-05T11:54:59.019465+00:00","updated_at":"2026-07-05T11:54:59.019465+00:00"}