{"as_of":"2026-08-15T19:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6cef6b95da6bfdb84f8a3840071d9b238f9c82350c0965c090d37e1d13d2049a","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:11:25.301259Z","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":"2026-05-22T15:21:44.958376Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-08-14T14:25:47.509676Z","title":null,"venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"1908.03151","last_updated":"2020-02-21T18:25:01Z","snapshot_observed_at":"2026-08-14T14:19:56.693308Z","submitted_at":"2019-08-08T16:21:27Z","title":"Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:25:47.509676Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/1908.03151"},"observation_digest":"sha256:4946f108700eff29068e872feead66bfd9430263dda84d64992c2e19597168b0","observation_id":"309a6b94-e5c6-4a3f-80c3-144f62f502ca","resolution":{"observed_at":"2026-08-14T14:25:47.509676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-08-14T05:05:21.569839Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1909.02143","last_updated":"2020-03-30T18:02:59Z","snapshot_observed_at":"2026-08-15T07:30:51.558484Z","submitted_at":"2019-09-04T22:37:15Z","title":"Analytic Waveforms for Eccentric Gravitational Wave Bursts","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T05:05:21.569839Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/1909.02143"},"observation_digest":"sha256:dfa144f9eaf8cc2f40fd9d6f40547aaa1bc7130d2b32438f806b7c9cfe9c5f77","observation_id":"769f7c60-3863-423d-be01-2dcd3833b9c6","resolution":{"observed_at":"2026-08-14T05:05:21.569839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":"1903.01998","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"298859da-2cbe-4fbc-a298-c3014b60ac4b","year":2022},"citing_paper":{"arxiv_id":"2505.08089","last_updated":"2026-07-08T09:19:56Z","snapshot_observed_at":"2026-08-13T01:33:41.236824Z","submitted_at":"2025-05-12T21:49:25Z","title":"Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-22T15:18:34.550100Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/2505.08089"},"observation_digest":"sha256:00d7cbc77ed785b003ef9675c8a872318b6b23a367cdda20ebe9f31d6988dee9","observation_id":"1c689283-6091-49d7-8ef9-8f9b600ab2ee","resolution":{"observed_at":"2026-05-22T15:21:44.960607Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":"1903.01998","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"298859da-2cbe-4fbc-a298-c3014b60ac4b","year":2022},"citing_paper":{"arxiv_id":"2505.20996","last_updated":"2026-05-11T16:32:51Z","snapshot_observed_at":"2026-08-13T06:53:40.517107Z","submitted_at":"2025-05-27T10:31:21Z","title":"Parameter inference of millilensed gravitational waves using neural spline flows","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-19T13:21:37.451964Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/2505.20996"},"observation_digest":"sha256:ec6b54facd704ca5b34b0b181b09df8198a6f3525a96228223748d787c0e9fd5","observation_id":"11b59a5c-8761-4063-94b4-4880bb33ce0a","resolution":{"observed_at":"2026-05-19T13:22:18.683795Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-08-15T19:11:25.301259Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.17618","last_updated":"2025-06-21T07:04:12Z","snapshot_observed_at":"2026-08-15T19:03:12.993893Z","submitted_at":"2025-06-21T07:04:12Z","title":"Black Hole Spectroscopy with Conditional Variational Autoencoder","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T19:11:25.301259Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/2506.17618"},"observation_digest":"sha256:709fc00726635c712d5d643f5338e23ca0afa801c18c08bc35f21d8d6bee931b","observation_id":"8f82e842-eed1-47c1-b045-84f903cb36c8","resolution":{"observed_at":"2026-08-15T19:11:25.301259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-07-11T23:16:00.672720Z","title":null,"venue":null,"work_id":null,"year":2022},"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":155,"source":"pdf_text","source_observed_at":"2026-07-11T23:16:00.672720Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/2607.03885"},"observation_digest":"sha256:4f068b048191d3030cb37cdf7de49421edf0093458cba09b62455b940a6f8c59","observation_id":"066e3c19-9ddb-4afb-b864-0a6a7c9183c0","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"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01998","snapshot_observed_at":"2026-08-06T00:42:42.626202Z","title":"Statistically-informed deep learning for gravi- tational wave parameter estimation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00934","last_updated":"2026-08-02T02:19:55Z","snapshot_observed_at":"2026-08-06T23:25:13.079313Z","submitted_at":"2026-08-02T02:19:55Z","title":"Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers","version":1},"reference_index":130,"source":"pdf_text","source_observed_at":"2026-08-06T00:42:42.626202Z"},"links":{"cited_paper":"/paper/1903.01998","citing_paper":"/paper/2608.00934"},"observation_digest":"sha256:559c93958bedfb5ec488fdf1c387b4a6188d534fb593648426a13c3f430fc10f","observation_id":"e6a1ed3e-5e9f-450c-aee0-a1d146848c48","resolution":{"observed_at":"2026-08-06T00:42:42.626202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1903.01998/citation-record","integrity":"/paper/1903.01998/integrity","json":"/paper/1903.01998/citation-record.json","paper":"/paper/1903.01998"},"outbound":[],"paper":{"arxiv_id":"1903.01998","last_updated":"2021-12-19T06:45:22Z","latest_version":4,"primary_category":"gr-qc","snapshot_observed_at":"2026-08-14T17:06:59.780328Z","submitted_at":"2019-03-05T19:00:02Z","title":"Statistically-informed deep learning for gravitational wave parameter estimation"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1903.01998."}