{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UZ2LDYJLFDTCLLPYLH5J65TL6P","short_pith_number":"pith:UZ2LDYJL","schema_version":"1.0","canonical_sha256":"a674b1e12b28e625adf859fa9f766bf3eace81c6983d76caf62f5045cb192288","source":{"kind":"arxiv","id":"2410.02061","version":2},"attestation_state":"computed","paper":{"title":"Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"J. Alberto V\\'azquez, Jos\\'e de Jes\\'us Vel\\'azquez, Luis A. Escamilla, Purba Mukherjee","submitted_at":"2024-10-02T22:16:13Z","abstract_excerpt":"The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and"},"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":"2410.02061","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2024-10-02T22:16:13Z","cross_cats_sorted":[],"title_canon_sha256":"bea41b17b4f4431a1dbc621ea51c82224a863c28fc381e28b2465609cd57325d","abstract_canon_sha256":"a5b20540c4b4e4375ee5f45906eeafeb942c910dbc4fd8d0535c7fe780f0fe5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:34.917058Z","signature_b64":"BTo6yGQ1zgZ1JN1GYXbZxgBKLjbgWcJyYvh1jYvB2LRpaLPoGzKm3SIkDkOjQOaBvysRd5lBeVsXZtJJBBSvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a674b1e12b28e625adf859fa9f766bf3eace81c6983d76caf62f5045cb192288","last_reissued_at":"2026-07-05T09:55:34.916551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:34.916551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"J. Alberto V\\'azquez, Jos\\'e de Jes\\'us Vel\\'azquez, Luis A. Escamilla, Purba Mukherjee","submitted_at":"2024-10-02T22:16:13Z","abstract_excerpt":"The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02061","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/2410.02061/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":"2410.02061","created_at":"2026-07-05T09:55:34.916611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02061v2","created_at":"2026-07-05T09:55:34.916611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02061","created_at":"2026-07-05T09:55:34.916611+00:00"},{"alias_kind":"pith_short_12","alias_value":"UZ2LDYJLFDTC","created_at":"2026-07-05T09:55:34.916611+00:00"},{"alias_kind":"pith_short_16","alias_value":"UZ2LDYJLFDTCLLPY","created_at":"2026-07-05T09:55:34.916611+00:00"},{"alias_kind":"pith_short_8","alias_value":"UZ2LDYJL","created_at":"2026-07-05T09:55:34.916611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.13427","citing_title":"Latent-Space Gaussian Processes for Dark-Energy Reconstruction from Observational \\(H(z)\\) Data","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25813","citing_title":"Geometric Constraints on the Pre-Recombination Expansion History from the Hubble Tension","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12987","citing_title":"Do equation of state parametrizations of dark energy faithfully capture the dynamics of the late universe?","ref_index":122,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P","json":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P.json","graph_json":"https://pith.science/api/pith-number/UZ2LDYJLFDTCLLPYLH5J65TL6P/graph.json","events_json":"https://pith.science/api/pith-number/UZ2LDYJLFDTCLLPYLH5J65TL6P/events.json","paper":"https://pith.science/paper/UZ2LDYJL"},"agent_actions":{"view_html":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P","download_json":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P.json","view_paper":"https://pith.science/paper/UZ2LDYJL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02061&json=true","fetch_graph":"https://pith.science/api/pith-number/UZ2LDYJLFDTCLLPYLH5J65TL6P/graph.json","fetch_events":"https://pith.science/api/pith-number/UZ2LDYJLFDTCLLPYLH5J65TL6P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P/action/storage_attestation","attest_author":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P/action/author_attestation","sign_citation":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P/action/citation_signature","submit_replication":"https://pith.science/pith/UZ2LDYJLFDTCLLPYLH5J65TL6P/action/replication_record"}},"created_at":"2026-07-05T09:55:34.916611+00:00","updated_at":"2026-07-05T09:55:34.916611+00:00"}