{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7KEI6FSVANSMUHQCPYIUXCWGJC","short_pith_number":"pith:7KEI6FSV","schema_version":"1.0","canonical_sha256":"fa888f16550364ca1e027e114b8ac648943e8ae4088dee59626688b83fe63453","source":{"kind":"arxiv","id":"2209.01113","version":2},"attestation_state":"computed","paper":{"title":"Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc"],"primary_cat":"astro-ph.CO","authors_text":"Jackson Levi Said, Jurgen Mifsud, Purba Mukherjee","submitted_at":"2022-09-02T15:23:40Z","abstract_excerpt":"In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain $H'(z)$, the first order derivative of $H(z)$, using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with"},"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":"2209.01113","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2022-09-02T15:23:40Z","cross_cats_sorted":["gr-qc"],"title_canon_sha256":"0958ac6591fc2db5e725d7c1a1393d4c3c69e848aad413d5dd99c714cdfb5cd9","abstract_canon_sha256":"b3aa12323461334870063a12e51c686f7fdd513b94ceb5fc780151cb196f1243"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:34.891366Z","signature_b64":"o4pXRm8yXgyQYMtxEMfGzIByAxVL8R04IJjSrrNqSv4TNP060Uit1Or6yvdEb8CBf0WFrVXBVZhXWhUKuhTADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa888f16550364ca1e027e114b8ac648943e8ae4088dee59626688b83fe63453","last_reissued_at":"2026-07-05T05:27:34.890870Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:34.890870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc"],"primary_cat":"astro-ph.CO","authors_text":"Jackson Levi Said, Jurgen Mifsud, Purba Mukherjee","submitted_at":"2022-09-02T15:23:40Z","abstract_excerpt":"In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain $H'(z)$, the first order derivative of $H(z)$, using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.01113","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/2209.01113/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":"2209.01113","created_at":"2026-07-05T05:27:34.890932+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.01113v2","created_at":"2026-07-05T05:27:34.890932+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.01113","created_at":"2026-07-05T05:27:34.890932+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KEI6FSVANSM","created_at":"2026-07-05T05:27:34.890932+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KEI6FSVANSMUHQC","created_at":"2026-07-05T05:27:34.890932+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KEI6FSV","created_at":"2026-07-05T05:27:34.890932+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2411.03773","citing_title":"Model-independent calibration of Gamma-Ray Bursts with neural networks","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22372","citing_title":"Testing $\\Lambda$CDM with ANN-Reconstructed Expansion History from Cosmic Chronometers","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC","json":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC.json","graph_json":"https://pith.science/api/pith-number/7KEI6FSVANSMUHQCPYIUXCWGJC/graph.json","events_json":"https://pith.science/api/pith-number/7KEI6FSVANSMUHQCPYIUXCWGJC/events.json","paper":"https://pith.science/paper/7KEI6FSV"},"agent_actions":{"view_html":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC","download_json":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC.json","view_paper":"https://pith.science/paper/7KEI6FSV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.01113&json=true","fetch_graph":"https://pith.science/api/pith-number/7KEI6FSVANSMUHQCPYIUXCWGJC/graph.json","fetch_events":"https://pith.science/api/pith-number/7KEI6FSVANSMUHQCPYIUXCWGJC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC/action/storage_attestation","attest_author":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC/action/author_attestation","sign_citation":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC/action/citation_signature","submit_replication":"https://pith.science/pith/7KEI6FSVANSMUHQCPYIUXCWGJC/action/replication_record"}},"created_at":"2026-07-05T05:27:34.890932+00:00","updated_at":"2026-07-05T05:27:34.890932+00:00"}