{"as_of":"2026-08-22T23:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:725dd98e48dba5c36d75d29b5d2426f82e3c1acfd2a2bddc46e6ea4f8915c9ab","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:41:41.420806Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T03:33:56.396686Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-08-12T12:42:57.097636Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17094","last_updated":"2025-09-10T14:55:29Z","snapshot_observed_at":"2026-08-17T11:06:11.899861Z","submitted_at":"2024-11-26T04:12:04Z","title":"Analytical modeling of the one-dimensional power spectrum of 21-cm forest based on a halo model method","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:57.097636Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2411.17094"},"observation_digest":"sha256:d5f9d2d4e8abf4c1c508fa4091ea2620a84cdd1c95db3bde180f25269a168afd","observation_id":"6956d4d0-15a0-4523-bbb3-2f383ebea67e","resolution":{"observed_at":"2026-08-12T12:42:57.097636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-08-11T19:26:46.146417Z","title":"arXiv:2407.14298","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06879","last_updated":"2025-01-17T13:20:39Z","snapshot_observed_at":"2026-08-16T20:17:44.099572Z","submitted_at":"2024-12-09T19:00:00Z","title":"Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization","version":2},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-11T19:26:46.146417Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2412.06879"},"observation_digest":"sha256:051029911dc80ddd5188eb0371191e0b92e8a20a7918625009bb556ef2e0a876","observation_id":"34a12bc9-7340-43de-85a9-a38555598e39","resolution":{"observed_at":"2026-08-11T19:26:46.146417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-08-10T22:48:21.459484Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00769","last_updated":"2025-02-26T10:16:34Z","snapshot_observed_at":"2026-08-17T11:06:51.363529Z","submitted_at":"2025-01-01T08:15:33Z","title":"Prospects for measuring neutrino mass with 21-cm forest","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T22:48:21.459484Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2501.00769"},"observation_digest":"sha256:ddde283fce99c14eec7b94439f198ea23abe113c2291bb5cfe4598aa3266bc46","observation_id":"703432a2-f277-4430-90b9-e40e703d3668","resolution":{"observed_at":"2026-08-10T22:48:21.459484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-08-16T11:41:41.420806Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.15086","last_updated":"2025-08-22T06:13:26Z","snapshot_observed_at":"2026-08-20T22:34:10.555815Z","submitted_at":"2025-04-21T13:20:16Z","title":"Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:41:41.420806Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2504.15086"},"observation_digest":"sha256:f61eb5d09f3ab9f7aa1bca27e61f1d49ea9918b4865cd9c939bd793269e8bf95","observation_id":"a22ba01b-a7c4-4789-8af2-e9362a966c5c","resolution":{"observed_at":"2026-08-16T11:41:41.420806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":"2407.14298","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b9624572-941b-4b8a-8389-a62e4170f271","year":2025},"citing_paper":{"arxiv_id":"2505.20996","last_updated":"2026-05-11T16:32:51Z","snapshot_observed_at":"2026-08-19T01:23:13.279064Z","submitted_at":"2025-05-27T10:31:21Z","title":"Parameter inference of millilensed gravitational waves using neural spline flows","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-19T13:21:37.451964Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2505.20996"},"observation_digest":"sha256:401b64500562d14705cce1e7ff51ada5f6dedaa5db71c1fd4cc4da077e59ecf2","observation_id":"bb7b9dd7-f68d-4d46-ab83-1ab55d7c8aad","resolution":{"observed_at":"2026-05-19T13:22:18.832637Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-08-04T16:19:49.916330Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.14751","last_updated":"2026-06-06T07:08:18Z","snapshot_observed_at":"2026-08-17T06:13:48.204071Z","submitted_at":"2025-09-18T08:54:47Z","title":"Probing initial isocurvature perturbation with 21cm one-point statistics","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T16:19:49.916330Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2509.14751"},"observation_digest":"sha256:eba28e2f48ae7ed831c7ac3f221dcebe220810907d5af9889e6fd32472c22961","observation_id":"208fc511-4827-41aa-9b7e-1ae58d2ddd7d","resolution":{"observed_at":"2026-08-04T16:19:49.916330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":"2407.14298","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b9624572-941b-4b8a-8389-a62e4170f271","year":2025},"citing_paper":{"arxiv_id":"2511.13092","last_updated":"2026-03-31T18:29:34Z","snapshot_observed_at":"2026-08-15T07:29:02.786259Z","submitted_at":"2025-11-17T07:48:01Z","title":"Topological Signatures of Heating and Dark Matter in the 21 cm Forest","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-17T21:18:19.245352Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2511.13092"},"observation_digest":"sha256:a4a0e0e3c7b9e61331feac329a09c0afe95c95cdf53cab01ecc39a696d5be6de","observation_id":"ed65552e-9946-4826-b1a1-14cb3123222f","resolution":{"observed_at":"2026-05-17T21:20:16.914981Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":"2407.14298","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b9624572-941b-4b8a-8389-a62e4170f271","year":2025},"citing_paper":{"arxiv_id":"2604.13867","last_updated":"2026-04-15T13:30:09Z","snapshot_observed_at":"2026-08-12T19:14:24.859123Z","submitted_at":"2026-04-15T13:30:09Z","title":"Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-10T12:44:04.023275Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2604.13867"},"observation_digest":"sha256:db4c5b68adaa853a510c23af3ff633087a04f4a44f8d2d1a328a5590b58a201a","observation_id":"5c017b17-8448-4d63-96a6-1005e87b8566","resolution":{"observed_at":"2026-05-10T12:45:24.084359Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":"2407.14298","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b9624572-941b-4b8a-8389-a62e4170f271","year":2025},"citing_paper":{"arxiv_id":"2605.21310","last_updated":"2026-05-20T15:37:58Z","snapshot_observed_at":"2026-08-17T02:55:31.042897Z","submitted_at":"2026-05-20T15:37:58Z","title":"Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection","version":1},"reference_index":136,"source":"pdf_text","source_observed_at":"2026-05-21T03:33:53.198336Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2605.21310"},"observation_digest":"sha256:d898e97e13d7b11c3dda68db48d4b51fbfa6262a682ca9c68b0a31995586fa9a","observation_id":"4bcec3cc-fd41-43f6-b0a9-a9c51dc8073e","resolution":{"observed_at":"2026-05-21T03:33:56.398180Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14298","snapshot_observed_at":"2026-07-11T23:16:00.672720Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03885","last_updated":"2026-07-04T14:02:26Z","snapshot_observed_at":"2026-08-15T04:57:25.727070Z","submitted_at":"2026-07-04T14:02:26Z","title":"Identifying lensed gravitational waves with physics-informed posterior learning","version":1},"reference_index":163,"source":"pdf_text","source_observed_at":"2026-07-11T23:16:00.672720Z"},"links":{"cited_paper":"/paper/2407.14298","citing_paper":"/paper/2607.03885"},"observation_digest":"sha256:18eb510418990721b3db7c60454ff8e52e7e7e9f97f695876a31c8d08522f9d1","observation_id":"6bd77c41-1819-4140-854d-431ab6542bc9","resolution":{"observed_at":"2026-07-11T23:16:00.672720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.14298/citation-record","integrity":"/paper/2407.14298/integrity","json":"/paper/2407.14298/citation-record.json","paper":"/paper/2407.14298"},"outbound":[],"paper":{"arxiv_id":"2407.14298","last_updated":"2025-05-20T11:22:42Z","latest_version":2,"primary_category":"astro-ph.CO","snapshot_observed_at":"2026-08-16T13:32:43.516768Z","submitted_at":"2024-07-19T13:30:40Z","title":"Deep learning-driven likelihood-free parameter inference for 21-cm forest observations"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2407.14298."}