{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","short_pith_number":"pith:WOOQN6QF","canonical_record":{"source":{"id":"2605.10105","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T07:19:36Z","cross_cats_sorted":[],"title_canon_sha256":"ff68fc7c1fd81c3087baff408fc5505f8a2fc30688a2634a70c375c5ea12748e","abstract_canon_sha256":"a458e968a7838e4ca230a68f74f2ca5aaa84089772944b7554ba5426daeca1e1"},"schema_version":"1.0"},"canonical_sha256":"b39d06fa056d9e3c7498481886a9c7834051931b991db81027e37d3756a8e495","source":{"kind":"arxiv","id":"2605.10105","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.10105","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.10105v3","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.10105","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"WOOQN6QFNWPD","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_16","alias_value":"WOOQN6QFNWPDY5EY","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_8","alias_value":"WOOQN6QF","created_at":"2026-05-20T00:00:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","target":"record","payload":{"canonical_record":{"source":{"id":"2605.10105","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T07:19:36Z","cross_cats_sorted":[],"title_canon_sha256":"ff68fc7c1fd81c3087baff408fc5505f8a2fc30688a2634a70c375c5ea12748e","abstract_canon_sha256":"a458e968a7838e4ca230a68f74f2ca5aaa84089772944b7554ba5426daeca1e1"},"schema_version":"1.0"},"canonical_sha256":"b39d06fa056d9e3c7498481886a9c7834051931b991db81027e37d3756a8e495","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:42.430519Z","signature_b64":"lKXAT+STwkzFNI7Rj2JlbgCQ3y/Or727yZMJynbyrhSCrk92uP/YBNAiGFuNETKEOr9GzvD7LCEr8gWWMY5mBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b39d06fa056d9e3c7498481886a9c7834051931b991db81027e37d3756a8e495","last_reissued_at":"2026-05-20T00:00:42.429909Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:42.429909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.10105","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6tIZsU+HDavhWeKYRT54a14Rdp2ocAPPOMcV/DhWjwiUjDIACjK+vTRbCfK+iUik3LJjbYRBs96NvKHifqRsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T07:14:06.674079Z"},"content_sha256":"c2e8cae91927054987fb5159df4edf2b56e47617dfdac4bebf15cbd4f69bf4d6","schema_version":"1.0","event_id":"sha256:c2e8cae91927054987fb5159df4edf2b56e47617dfdac4bebf15cbd4f69bf4d6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of Machine Learning to 21 cm Cosmology","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges.","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Hayato Shimabukuro","submitted_at":"2026-05-11T07:19:36Z","abstract_excerpt":"In this chapter, the use of machine learning (ML) in redshifted 21 cm cosmology is discussed, especially for the cosmic dawn, the Epoch of Reionization, and the scientific program of SKA-Low. The 21 cm signal is useful because it can directly probe diffuse neutral hydrogen. At the same time, it is a difficult signal, since the observable depends on density, ionization, heating, radiation backgrounds, and instrumental response in a nonlinear way. The first part of this chapter reviews the basic physical ingredients needed for the later discussion, including the global signal, spatial fluctuatio"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"ML applications are grouped by their role in the analysis pipeline: observation-domain methods work on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological constraints.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The review assumes that existing machine learning techniques can be effectively adapted to the nonlinear, multi-physics nature of the 21 cm signal without introducing uncontrolled biases from training data or model choices.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"dda69e7689e4c69f03c88d343968ab13160fa160e36f269242248bffb16e6a80"},"source":{"id":"2605.10105","kind":"arxiv","version":3},"verdict":{"id":"028d4603-1be3-493d-b1e3-e862fbcbf0df","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T17:36:14.233897Z","strongest_claim":"ML applications are grouped by their role in the analysis pipeline: observation-domain methods work on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological constraints.","one_line_summary":"A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The review assumes that existing machine learning techniques can be effectively adapted to the nonlinear, multi-physics nature of the 21 cm signal without introducing uncontrolled biases from training data or model choices.","pith_extraction_headline":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":1,"by_detector":{"doi_compliance":{"total":2,"advisory":1,"critical":1,"informational":0}},"informational":0},"endpoint":"/pith/2605.10105/integrity.json","findings":[{"note":"Identifier '10.1016/j.ascom.2016.12.002' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detector":"doi_compliance","severity":"critical","ref_index":3,"audited_at":"2026-05-19T09:40:04.363790Z","detected_doi":"10.1016/j.ascom.2016.12.002","finding_type":"unresolvable_identifier","verdict_class":"cross_source","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1111/j.1365-2966.2006.10603.x1) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":18,"audited_at":"2026-05-19T09:40:04.363790Z","detected_doi":"10.1111/j.1365-2966.2006.10603.x1","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T15:40:30.834484Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T12:01:17.779025Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T09:40:04.363790Z","status":"completed","version":"1.0.0","findings_count":2}],"snapshot_sha256":"af0a1a09b50f746384f79cbbfb59cb745c10676ab072199af7104a732bc736d2"},"references":{"count":134,"sample":[{"doi":"10.3847/1538-4357/acaf50","year":2023,"title":"Abdurashidova, Z., Adams, T., Aguirre, J. E., et al. 2023, ApJ, 945, 124. doi:10.3847/1538-4357/acaf50","work_id":"c2d9fc19-9c55-48be-b6e5-e71671b1e5f6","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1093/mnras/stad3701","year":null,"title":"2024a, MNRAS, 527, 3","work_id":"2425eac8-7fac-48a6-aee1-23cb6b27d0df","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1093/mnrasl/slae078","year":null,"title":"2024b, MNRAS, 534, 1, L30","work_id":"235ff1b5-4aa4-4604-b705-b61b01545bb9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/j.ascom.2016.12.002","year":2017,"title":"2017, Astronomy and Com- puting, 18, 35","work_id":"f69d471b-2c67-4263-a709-5cd2cd73e16c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"2019, MNRAS, 488, 3","work_id":"2285ae33-e7e3-4698-9819-27c2dc177421","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":134,"snapshot_sha256":"76b178689f0c42a4173653e3d0134829240d0a023b0b3b56dc7e9ef512efa1df","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bc1489690a1904bf68a11b80a8fa3b274ceee23ecc47811d056161adb0d1e5fc"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"028d4603-1be3-493d-b1e3-e862fbcbf0df"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6yoVB4L89ueD8lMTDt8HIZ3thEHJnwj+pdjq5WiVU+G6oTPtIBcXzgtM2Wtza+FVWWb5+59+nVlD0bHzAyWBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T07:14:06.674866Z"},"content_sha256":"061cf1da198489013a129ba996008105ee462bb09b1c7f04b0bd5954c8209eda","schema_version":"1.0","event_id":"sha256:061cf1da198489013a129ba996008105ee462bb09b1c7f04b0bd5954c8209eda"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1111/j.1365-2966.2006.10603.x1) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Furlanetto, S. R. 2006, MNRAS, 370, 4, 1867. doi:10.1111/j.1365- 2966.2006.10603.x 1 Application of Machine Learning to 21 cm Cosmology 17","arxiv_id":"2605.10105","detector":"doi_compliance","evidence":{"ref_index":18,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Furlanetto, S. R. 2006, MNRAS, 370, 4, 1867. doi:10.1111/j.1365- 2966.2006.10603.x 1 Application of Machine Learning to 21 cm Cosmology 17","reconstructed_doi":"10.1111/j.1365-2966.2006.10603.x1"},"severity":"advisory","ref_index":18,"audited_at":"2026-05-19T09:40:04.363790Z","event_type":"pith.integrity.v1","detected_doi":"10.1111/j.1365-2966.2006.10603.x1","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"cfd32be4cca5107d3c23a93caaa56820670d613870142a6fe49f43b587161eb7","paper_version":2,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":671,"payload_sha256":"27a917bcf964879afbea203e7c9ba0a6437b9f560a02a469de65772d01006999","signature_b64":"4FFe/uhMdx2cgtVdIjpI6lT5/naI1SkI/wVfXADXG/q20GuQU4mPKdYZiOFAoSSl4jA/tueM29fYYk8NsLwODA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T09:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F8s8aYG/zzZWNutRlTkwKPrGGm0jOrjnY+dd0p+hKMkC2P6dj+FyhDyUaGAsAIInjD1ftEjlBkDCt7Q0sJ85AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T07:14:06.676140Z"},"content_sha256":"6baa3f5adabc3f6ea3bcf23061dcd10f9195cd1beeeea155a06f3c7b7dbb2c78","schema_version":"1.0","event_id":"sha256:6baa3f5adabc3f6ea3bcf23061dcd10f9195cd1beeeea155a06f3c7b7dbb2c78"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","target":"integrity","payload":{"note":"Identifier '10.1016/j.ascom.2016.12.002' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Akeret, J., Chang, C., Lucchi, A., & Refregier, A. 2017, Astronomy and Com- puting, 18, 35. doi:10.1016/j.ascom.2016.12.002","arxiv_id":"2605.10105","detector":"doi_compliance","evidence":{"doi":"10.1016/j.ascom.2016.12.002","arxiv_id":null,"ref_index":3,"raw_excerpt":"Akeret, J., Chang, C., Lucchi, A., & Refregier, A. 2017, Astronomy and Com- puting, 18, 35. doi:10.1016/j.ascom.2016.12.002","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":3,"audited_at":"2026-05-19T09:40:04.363790Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.ascom.2016.12.002","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"3819d973c7893d830f5fbe2aec90affd5570a35ed93089a003ba6ff7c21318d3","paper_version":2,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":670,"payload_sha256":"6deacba36b28b0a876057c463627e042f6350cb91d498c7a4242f22f8e2faf2f","signature_b64":"yjADbUBxF2hKvg/Gc2lWlFj4qg1L2cPlyebTO1Cw0hob8m+3iUjmGufKp2+CbaWxrnuLcClWkhof0wbUUngiBQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T09:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FYNMCl6yZUu0f7M7PaHfJJxaQsX6qneFKq5Nu7mrILJe7E8bStqdv8d/ZJXHmHO+ICw88SuS4Ll/YMaH1GjeAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T07:14:06.676472Z"},"content_sha256":"c796eea82f25cf89e14793eb4b43b8f41474d1febe0a56dc02153cc491a8cf40","schema_version":"1.0","event_id":"sha256:c796eea82f25cf89e14793eb4b43b8f41474d1febe0a56dc02153cc491a8cf40"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/bundle.json","state_url":"https://pith.science/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-22T07:14:06Z","links":{"resolver":"https://pith.science/pith/WOOQN6QFNWPDY5EYJAMINKOHQN","bundle":"https://pith.science/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/bundle.json","state":"https://pith.science/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WOOQN6QFNWPDY5EYJAMINKOHQN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WOOQN6QFNWPDY5EYJAMINKOHQN","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a458e968a7838e4ca230a68f74f2ca5aaa84089772944b7554ba5426daeca1e1","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T07:19:36Z","title_canon_sha256":"ff68fc7c1fd81c3087baff408fc5505f8a2fc30688a2634a70c375c5ea12748e"},"schema_version":"1.0","source":{"id":"2605.10105","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.10105","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.10105v3","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.10105","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"WOOQN6QFNWPD","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_16","alias_value":"WOOQN6QFNWPDY5EY","created_at":"2026-05-20T00:00:42Z"},{"alias_kind":"pith_short_8","alias_value":"WOOQN6QF","created_at":"2026-05-20T00:00:42Z"}],"graph_snapshots":[{"event_id":"sha256:061cf1da198489013a129ba996008105ee462bb09b1c7f04b0bd5954c8209eda","target":"graph","created_at":"2026-05-20T00:00:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"ML applications are grouped by their role in the analysis pipeline: observation-domain methods work on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological constraints."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The review assumes that existing machine learning techniques can be effectively adapted to the nonlinear, multi-physics nature of the 21 cm signal without introducing uncontrolled biases from training data or model choices."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges."}],"snapshot_sha256":"dda69e7689e4c69f03c88d343968ab13160fa160e36f269242248bffb16e6a80"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bc1489690a1904bf68a11b80a8fa3b274ceee23ecc47811d056161adb0d1e5fc"},"integrity":{"available":true,"clean":false,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T15:40:30.834484Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T12:01:17.779025Z","status":"completed","version":"1.0.0"},{"findings_count":2,"name":"doi_compliance","ran_at":"2026-05-19T09:40:04.363790Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.10105/integrity.json","findings":[{"audited_at":"2026-05-19T09:40:04.363790Z","detected_arxiv_id":null,"detected_doi":"10.1016/j.ascom.2016.12.002","detector":"doi_compliance","finding_type":"unresolvable_identifier","note":"Identifier '10.1016/j.ascom.2016.12.002' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","ref_index":3,"severity":"critical","verdict_class":"cross_source"},{"audited_at":"2026-05-19T09:40:04.363790Z","detected_arxiv_id":null,"detected_doi":"10.1111/j.1365-2966.2006.10603.x1","detector":"doi_compliance","finding_type":"recoverable_identifier","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1111/j.1365-2966.2006.10603.x1) was visible in the surrounding text but could not be confirmed against doi.org as printed.","ref_index":18,"severity":"advisory","verdict_class":"incontrovertible"}],"snapshot_sha256":"af0a1a09b50f746384f79cbbfb59cb745c10676ab072199af7104a732bc736d2","summary":{"advisory":1,"by_detector":{"doi_compliance":{"advisory":1,"critical":1,"informational":0,"total":2}},"critical":1,"informational":0}},"paper":{"abstract_excerpt":"In this chapter, the use of machine learning (ML) in redshifted 21 cm cosmology is discussed, especially for the cosmic dawn, the Epoch of Reionization, and the scientific program of SKA-Low. The 21 cm signal is useful because it can directly probe diffuse neutral hydrogen. At the same time, it is a difficult signal, since the observable depends on density, ionization, heating, radiation backgrounds, and instrumental response in a nonlinear way. The first part of this chapter reviews the basic physical ingredients needed for the later discussion, including the global signal, spatial fluctuatio","authors_text":"Hayato Shimabukuro","cross_cats":[],"headline":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges.","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T07:19:36Z","title":"Application of Machine Learning to 21 cm Cosmology"},"references":{"count":134,"internal_anchors":2,"resolved_work":134,"sample":[{"cited_arxiv_id":"","doi":"10.3847/1538-4357/acaf50","is_internal_anchor":false,"ref_index":1,"title":"Abdurashidova, Z., Adams, T., Aguirre, J. E., et al. 2023, ApJ, 945, 124. doi:10.3847/1538-4357/acaf50","work_id":"c2d9fc19-9c55-48be-b6e5-e71671b1e5f6","year":2023},{"cited_arxiv_id":"","doi":"10.1093/mnras/stad3701","is_internal_anchor":false,"ref_index":2,"title":"2024a, MNRAS, 527, 3","work_id":"2425eac8-7fac-48a6-aee1-23cb6b27d0df","year":null},{"cited_arxiv_id":"","doi":"10.1093/mnrasl/slae078","is_internal_anchor":false,"ref_index":3,"title":"2024b, MNRAS, 534, 1, L30","work_id":"235ff1b5-4aa4-4604-b705-b61b01545bb9","year":null},{"cited_arxiv_id":"","doi":"10.1016/j.ascom.2016.12.002","is_internal_anchor":false,"ref_index":4,"title":"2017, Astronomy and Com- puting, 18, 35","work_id":"f69d471b-2c67-4263-a709-5cd2cd73e16c","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"2019, MNRAS, 488, 3","work_id":"2285ae33-e7e3-4698-9819-27c2dc177421","year":2019}],"snapshot_sha256":"76b178689f0c42a4173653e3d0134829240d0a023b0b3b56dc7e9ef512efa1df"},"source":{"id":"2605.10105","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-19T17:36:14.233897Z","id":"028d4603-1be3-493d-b1e3-e862fbcbf0df","model_set":{"reader":"grok-4.3"},"one_line_summary":"A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Machine learning applications in 21 cm cosmology are grouped by pipeline role to handle the signal's nonlinear physical dependencies and analysis challenges.","strongest_claim":"ML applications are grouped by their role in the analysis pipeline: observation-domain methods work on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological constraints.","weakest_assumption":"The review assumes that existing machine learning techniques can be effectively adapted to the nonlinear, multi-physics nature of the 21 cm signal without introducing uncontrolled biases from training data or model choices."}},"verdict_id":"028d4603-1be3-493d-b1e3-e862fbcbf0df"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c2e8cae91927054987fb5159df4edf2b56e47617dfdac4bebf15cbd4f69bf4d6","target":"record","created_at":"2026-05-20T00:00:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a458e968a7838e4ca230a68f74f2ca5aaa84089772944b7554ba5426daeca1e1","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2026-05-11T07:19:36Z","title_canon_sha256":"ff68fc7c1fd81c3087baff408fc5505f8a2fc30688a2634a70c375c5ea12748e"},"schema_version":"1.0","source":{"id":"2605.10105","kind":"arxiv","version":3}},"canonical_sha256":"b39d06fa056d9e3c7498481886a9c7834051931b991db81027e37d3756a8e495","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b39d06fa056d9e3c7498481886a9c7834051931b991db81027e37d3756a8e495","first_computed_at":"2026-05-20T00:00:42.429909Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:42.429909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lKXAT+STwkzFNI7Rj2JlbgCQ3y/Or727yZMJynbyrhSCrk92uP/YBNAiGFuNETKEOr9GzvD7LCEr8gWWMY5mBg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:42.430519Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.10105","source_kind":"arxiv","source_version":3}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:6baa3f5adabc3f6ea3bcf23061dcd10f9195cd1beeeea155a06f3c7b7dbb2c78","sha256:c796eea82f25cf89e14793eb4b43b8f41474d1febe0a56dc02153cc491a8cf40"]}],"invalid_events":[],"applied_event_ids":["sha256:c2e8cae91927054987fb5159df4edf2b56e47617dfdac4bebf15cbd4f69bf4d6","sha256:061cf1da198489013a129ba996008105ee462bb09b1c7f04b0bd5954c8209eda"],"state_sha256":"73fe37e0b6465dda933ef8e0e315f5fa6d3d3211dbd3c2aa346a2a6cb4c9b22b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tIHERje+xVlZt3FwH7RdCAX4+O5prRgYXpzKB15lRm1mTqnUqakCRD9+P/vz6zo3pMH9W9quZ0VTec8kYL8nDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T07:14:06.679561Z","bundle_sha256":"c2f2cf08d6edef534b2093f34dc7e844ff45b0c912d62ee3a5a7165bcd0bc94f"}}