{"as_of":"2026-08-11T02:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3903453a9db51db29b454f417f9f4f3d306c0818b5b5a774aed3f9d569def586","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":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T00:33:04.551171Z","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":"2026-06-30T08:14:26.051987Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":"2008.03312","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-06-30T08:14:26.051987Z","title":null,"venue":null,"work_id":"59e5255c-e960-4a53-b816-88d203b482c7","year":2021},"citing_paper":{"arxiv_id":"2404.14286","last_updated":"2026-05-05T08:23:34Z","snapshot_observed_at":"2026-08-08T10:26:27.851868Z","submitted_at":"2024-04-22T15:37:08Z","title":"Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA","version":3},"reference_index":154,"source":"pdf_text","source_observed_at":"2026-05-24T01:34:53.946159Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2404.14286"},"observation_digest":"sha256:2f548220ae2ae8e45cdfed74339e375fd3ea59a8fc2b5394c7f422b95178f9c1","observation_id":"f5054c8f-b89f-4bfd-a7a4-5ac6ed863690","resolution":{"observed_at":"2026-05-24T01:35:56.115221Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":"2008.03312","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-06-30T08:14:26.051987Z","title":null,"venue":null,"work_id":"59e5255c-e960-4a53-b816-88d203b482c7","year":2021},"citing_paper":{"arxiv_id":"2505.20996","last_updated":"2026-05-11T16:32:51Z","snapshot_observed_at":"2026-08-04T14:17:18.545095Z","submitted_at":"2025-05-27T10:31:21Z","title":"Parameter inference of millilensed gravitational waves using neural spline flows","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-19T13:21:37.451964Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2505.20996"},"observation_digest":"sha256:efd427eceae2e1f3eaea997883cee0988bb3c5f90d9f05c7855c3304dc09ffa7","observation_id":"3a8522d3-3f04-43c1-bd3b-9dba9f460d6f","resolution":{"observed_at":"2026-05-19T13:22:18.590240Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-07T13:33:27.715351Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21612","last_updated":"2025-09-02T08:12:10Z","snapshot_observed_at":"2026-08-10T22:52:25.879604Z","submitted_at":"2025-05-27T18:00:00Z","title":"Revisiting GW150914 with a non-planar, eccentric waveform model","version":2},"reference_index":160,"source":"pdf_text","source_observed_at":"2026-08-07T13:33:27.715351Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2505.21612"},"observation_digest":"sha256:b4581fa08310a7072f007e34dff7011d4ead9ca9c5d5f2615f81cd014868c41f","observation_id":"ac0fca1f-9f99-4dbe-8860-0d21a9ad1b9b","resolution":{"observed_at":"2026-08-07T13:33:27.715351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-05T10:35:01.735201Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.04538","last_updated":"2025-09-04T08:29:05Z","snapshot_observed_at":"2026-08-07T23:21:09.088332Z","submitted_at":"2025-09-04T08:29:05Z","title":"Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T10:35:01.735201Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2509.04538"},"observation_digest":"sha256:f7a165da17ad52326c1a0c6792412f9886499acafc4504f114052672674ece21","observation_id":"e5258dba-a3b9-4ae6-a5dc-d5fdf0b08487","resolution":{"observed_at":"2026-08-05T10:35:01.735201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-03T22:55:47.691065Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.08486","last_updated":"2026-06-01T07:28:08Z","snapshot_observed_at":"2026-08-06T23:45:29.322816Z","submitted_at":"2025-11-11T17:26:43Z","title":"Accelerated inference of microlensed gravitational waves with machine learning","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-03T22:55:47.691065Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2511.08486"},"observation_digest":"sha256:847c0ad0bb895e049e1cff3a2ceb169e3c4321d5665b409cb36faeb1ac2efeab","observation_id":"f7c16695-2d84-424c-8be9-d759267c59c0","resolution":{"observed_at":"2026-08-03T22:55:47.691065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":"2008.03312","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-06-30T08:14:26.051987Z","title":null,"venue":null,"work_id":"59e5255c-e960-4a53-b816-88d203b482c7","year":2021},"citing_paper":{"arxiv_id":"2511.16879","last_updated":"2026-04-03T15:41:29Z","snapshot_observed_at":"2026-07-31T18:29:34.718365Z","submitted_at":"2025-11-21T01:08:52Z","title":"Accelerating parameter estimation for parameterized tests of general relativity with gravitational-wave observations","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-17T21:28:45.671170Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2511.16879"},"observation_digest":"sha256:5b5389de4e02329727dc6a2fc1140a4e9618ecc0d7f2bc63ddc89cf8aacc6404","observation_id":"5a666a18-7918-4051-ba96-f915216fa56f","resolution":{"observed_at":"2026-05-17T21:30:17.858809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-03T18:59:10.393322Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.02968","last_updated":"2026-06-24T08:39:18Z","snapshot_observed_at":"2026-08-08T12:02:11.867263Z","submitted_at":"2025-12-02T17:49:08Z","title":"Flexible Gravitational-Wave Parameter Estimation with Transformers","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T18:59:10.393322Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2512.02968"},"observation_digest":"sha256:ab15344246a0b125f6e281b05f030ea86ee41adee53c533cdd96c2ae8adadb99","observation_id":"34d0a2ab-dd4e-4c50-8fe6-5d02238b80fb","resolution":{"observed_at":"2026-08-03T18:59:10.393322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-03T15:28:14.485096Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.16916","last_updated":"2026-07-22T06:31:26Z","snapshot_observed_at":"2026-08-07T13:42:40.905832Z","submitted_at":"2025-12-18T18:59:53Z","title":"Discovering gravitational waveform distortions from lensing: A deep dive into GW231123","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T15:28:14.485096Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2512.16916"},"observation_digest":"sha256:205115c211a36a92849d60b26a89160cd5137c70dd4e6e3bb3cde6cab7ce3992","observation_id":"95a7dfff-178c-403e-9527-d2f70c664a87","resolution":{"observed_at":"2026-08-03T15:28:14.485096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":"2008.03312","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-06-30T08:14:26.051987Z","title":null,"venue":null,"work_id":"59e5255c-e960-4a53-b816-88d203b482c7","year":2021},"citing_paper":{"arxiv_id":"2605.21310","last_updated":"2026-05-20T15:37:58Z","snapshot_observed_at":"2026-08-02T18:34:09.495996Z","submitted_at":"2026-05-20T15:37:58Z","title":"Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection","version":1},"reference_index":124,"source":"pdf_text","source_observed_at":"2026-05-21T03:33:53.198336Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2605.21310"},"observation_digest":"sha256:3827aaccd6bbb15d016bc1a9bd44cd97958b34a380762bf6fdd620773f4dbb0c","observation_id":"fbb21ef0-67d5-4891-8a8f-9b2fbbf59766","resolution":{"observed_at":"2026-05-21T03:33:56.275675Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-02T11:36:01.615522Z","title":"Complete param- eter inference for GW150914 using deep learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.14229","last_updated":"2026-07-24T07:15:23Z","snapshot_observed_at":"2026-08-10T00:52:40.152165Z","submitted_at":"2026-06-12T08:11:12Z","title":"Fortifying gravitational-wave population inference with normalizing flows","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-02T11:36:01.615522Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2606.14229"},"observation_digest":"sha256:a9c1bbed8504e75120741bda99062015de72b0f40b5b0a75f64784d1143115da","observation_id":"a5db1f33-d0b7-47a9-b69a-f4d5846cfeca","resolution":{"observed_at":"2026-08-02T11:36:01.615522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":"2008.03312","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-06-30T08:14:26.051987Z","title":null,"venue":null,"work_id":"59e5255c-e960-4a53-b816-88d203b482c7","year":2021},"citing_paper":{"arxiv_id":"2606.29039","last_updated":"2026-06-27T18:20:00Z","snapshot_observed_at":"2026-08-06T02:19:12.382467Z","submitted_at":"2026-06-27T18:20:00Z","title":"Neural posterior estimation of Galactic Binary signals for the LISA mission","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T08:04:25.273338Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2606.29039"},"observation_digest":"sha256:be78b6ec70ea75edd2ea6f2eee33d6e2d3b61b64d37c0e0f4f443a618b81dbb3","observation_id":"2db939c4-d49e-4111-8c38-7f6fd94042d4","resolution":{"observed_at":"2026-06-30T08:14:26.053728Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-07-11T23:16:00.672720Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.03885","last_updated":"2026-07-04T14:02:26Z","snapshot_observed_at":"2026-08-06T02:37:02.500007Z","submitted_at":"2026-07-04T14:02:26Z","title":"Identifying lensed gravitational waves with physics-informed posterior learning","version":1},"reference_index":151,"source":"pdf_text","source_observed_at":"2026-07-11T23:16:00.672720Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2607.03885"},"observation_digest":"sha256:e60edcf1ee55bd498c9abee683d8d648eb22d340701704d92b3e3fc5df287c34","observation_id":"76a724eb-007c-4b20-96aa-fb2c4cde1119","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":"2008.03312","last_updated":"2020-08-07T18:00:02Z","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03312","snapshot_observed_at":"2026-08-11T00:33:04.551171Z","title":"and Gair, Jonathan","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.07667","last_updated":"2026-08-07T18:00:06Z","snapshot_observed_at":"2026-08-11T02:14:18.586663Z","submitted_at":"2026-08-07T18:00:06Z","title":"Probability of gravitational-wave lensing by intermediate-mass black holes and globular clusters","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-11T00:33:04.551171Z"},"links":{"cited_paper":"/paper/2008.03312","citing_paper":"/paper/2608.07667"},"observation_digest":"sha256:01212056be02ebc3e3991c5f93ef12789968a61459141349c91b9677da7bf95e","observation_id":"d0b19c94-509b-4d4b-b8a4-0f7324deb2fc","resolution":{"observed_at":"2026-08-11T00:33:04.551171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2008.03312/citation-record","integrity":"/paper/2008.03312/integrity","json":"/paper/2008.03312/citation-record.json","paper":"/paper/2008.03312"},"outbound":[],"paper":{"arxiv_id":"2008.03312","last_updated":"2020-08-07T18:00:02Z","latest_version":1,"primary_category":"astro-ph.IM","snapshot_observed_at":"2026-08-04T17:53:45.897086Z","submitted_at":"2020-08-07T18:00:02Z","title":"Complete parameter inference for GW150914 using deep learning"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2008.03312."}