{"as_of":"2026-08-18T04:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9817d14b19d4ccbfd4caa59c6f538048fbbbf6ceb5236d055f55178d7d12bd83","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":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":26,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:35:18.589925Z","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":"2026-07-04T07:29:38.635470Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-11T15:57:43.309638Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10501","last_updated":"2025-02-16T22:09:02Z","snapshot_observed_at":"2026-08-14T12:05:14.908014Z","submitted_at":"2024-12-13T19:00:02Z","title":"Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:57:43.309638Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2412.10501"},"observation_digest":"sha256:d0cd3b0a7b1e3862aa1151980bd29ace961bb41d9dd50a1eaa6e81712b3261df","observation_id":"25f7f51a-507f-4963-86ad-f908de23ad9f","resolution":{"observed_at":"2026-08-11T15:57:43.309638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-11T10:45:19.450394Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16372","last_updated":"2024-12-20T22:08:27Z","snapshot_observed_at":"2026-08-17T23:36:48.673166Z","submitted_at":"2024-12-20T22:08:27Z","title":"Angular Resolution of a Bayesian Search for Anisotropic Stochastic Gravitational Wave Backgrounds with LISA","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T10:45:19.450394Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2412.16372"},"observation_digest":"sha256:d09975dae98a6975a2f04894e7725aba78bafab1dc51fd8a6244890eab1a01ec","observation_id":"b71ea9bd-5c01-41a2-bda4-5be15305d4dd","resolution":{"observed_at":"2026-08-11T10:45:19.450394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-10T19:21:48.765983Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.10277","last_updated":"2025-04-15T12:05:45Z","snapshot_observed_at":"2026-08-11T03:51:44.192214Z","submitted_at":"2025-01-17T16:08:30Z","title":"Modular global-fit pipeline for LISA data analysis","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T19:21:48.765983Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2501.10277"},"observation_digest":"sha256:984c85cbb79fde7ce0fcc4d8426ceeea60c37b0d3f2e55079cc70a60fe745d88","observation_id":"f48b6a08-b969-490d-9af1-bb9ddedf4f2c","resolution":{"observed_at":"2026-08-10T19:21:48.765983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-10T18:29:59.912067Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.11320","last_updated":"2025-05-21T08:43:23Z","snapshot_observed_at":"2026-08-18T04:13:18.823853Z","submitted_at":"2025-01-20T07:47:31Z","title":"Reconstructing Primordial Curvature Perturbations via Scalar-Induced Gravitational Waves with LISA","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T18:29:59.912067Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2501.11320"},"observation_digest":"sha256:c65a3e2e3ff5e5bf7a6a9843a8cbd542d5c7e5f74e481c064c1017fd3efb5ac9","observation_id":"7a367644-e01a-4b69-bfbe-7d5c4eb07da4","resolution":{"observed_at":"2026-08-10T18:29:59.912067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T19:32:10.074131Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.10087","last_updated":"2025-05-27T19:19:07Z","snapshot_observed_at":"2026-08-13T23:47:51.639045Z","submitted_at":"2025-02-14T11:19:27Z","title":"The implications of stochastic gas torques for asymmetric binaries in the LISA band","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T19:32:10.074131Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2502.10087"},"observation_digest":"sha256:9fad4dc1ccc2875452c3ca4a02eb9290cc4943642dea52574573a1536435bc64","observation_id":"8a9c81b4-fefc-455f-a97f-17368e243ac6","resolution":{"observed_at":"2026-08-07T19:32:10.074131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-15T21:35:18.589925Z","title":"Effi- cient GPU-accelerated multisource global fit pipeline for LISA data analysis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.09600","last_updated":"2025-08-14T10:23:59Z","snapshot_observed_at":"2026-08-15T21:25:17.975703Z","submitted_at":"2025-05-14T17:50:28Z","title":"Accelerating the time-domain LISA response model with central finite differences and hybridization techniques","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:35:18.589925Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2505.09600"},"observation_digest":"sha256:094e9d79644ed69b11450dc398bf138dbcdd097af34bb981621f9bf9b22fde0d","observation_id":"dc6ffc27-4cb6-4bc8-b40d-c567bb0d71f9","resolution":{"observed_at":"2026-08-15T21:35:18.589925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T14:59:19.588503Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.16795","last_updated":"2026-06-16T15:56:02Z","snapshot_observed_at":"2026-08-14T09:16:03.826739Z","submitted_at":"2025-05-22T15:36:49Z","title":"Sequential simulation-based inference for extreme mass ratio inspirals","version":2},"reference_index":120,"source":"pdf_text","source_observed_at":"2026-08-07T14:59:19.588503Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2505.16795"},"observation_digest":"sha256:d34106185b06d97bffb1a05018945fc563dc98ab8e6533119e033c2a888100d3","observation_id":"bc82d4ab-36da-4fe8-ac73-c5a431e8e900","resolution":{"observed_at":"2026-08-07T14:59:19.588503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T14:14:27.722396Z","title":"An efficient gpu-accelerated multi- source global fit pipeline for lisa data analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19870","last_updated":"2025-09-12T18:10:15Z","snapshot_observed_at":"2026-08-17T12:00:37.354443Z","submitted_at":"2025-05-26T11:55:44Z","title":"A pipeline for searching and fitting instrumental glitches in LISA data","version":4},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T14:14:27.722396Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2505.19870"},"observation_digest":"sha256:8e64c3d1296c510bec97ac739de9e69b53268dae419360d4bc1a9dddf0947011","observation_id":"2bb23aa0-67f3-4102-9612-84175684c8b9","resolution":{"observed_at":"2026-08-07T14:14:27.722396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T11:37:23.070184Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.01898","last_updated":"2025-07-23T13:34:56Z","snapshot_observed_at":"2026-08-10T04:51:16.544163Z","submitted_at":"2025-06-02T17:21:37Z","title":"Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:23.070184Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2506.01898"},"observation_digest":"sha256:54a8ac812dc18a83e11a851e0b451b46bfa01042cfc7413033848c76d3427642","observation_id":"711583ff-029f-4993-bc70-6827dff5f4e3","resolution":{"observed_at":"2026-08-07T11:37:23.070184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T05:56:21.982257Z","title":"Katz, Nikolaos Karnesis, Natalia Korsakova, et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06783","last_updated":"2025-08-01T13:37:29Z","snapshot_observed_at":"2026-08-15T20:47:22.858461Z","submitted_at":"2025-06-07T12:51:38Z","title":"Across the Horizon: On Gravitational Wave Flux Laws and Tests of Gravity","version":3},"reference_index":180,"source":"pdf_text","source_observed_at":"2026-08-07T05:56:21.982257Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2506.06783"},"observation_digest":"sha256:c44a0e62af678eab4deae13aa9506fe4ae3d8d7643f8f5a85b05a1d23abe7841","observation_id":"4795d657-5d0c-4765-a01e-6c55ea376026","resolution":{"observed_at":"2026-08-07T05:56:21.982257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-07T04:28:50.495462Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10599","last_updated":"2025-06-20T12:43:30Z","snapshot_observed_at":"2026-08-15T20:00:22.685658Z","submitted_at":"2025-06-12T11:43:40Z","title":"Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:28:50.495462Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2506.10599"},"observation_digest":"sha256:4c83a6333e06cc2ad553bd55bc7dcf82126f93b714b971e5308036627ec056af","observation_id":"cc46e73f-dbee-4e37-86b7-a10f494849e6","resolution":{"observed_at":"2026-08-07T04:28:50.495462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-15T18:50:34.688401Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18965","last_updated":"2025-06-23T18:00:00Z","snapshot_observed_at":"2026-08-16T18:28:51.400504Z","submitted_at":"2025-06-23T18:00:00Z","title":"A Fog Over the Cosmological SGWB: Unresolved Massive Black Hole Binaries in the LISA Band","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:34.688401Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2506.18965"},"observation_digest":"sha256:6b602b4ff0e90142595f9eb236f2aca5c71e557910e13c37faa4558fec4132b1","observation_id":"d47c40d9-cf11-45f6-9510-0418d44e9339","resolution":{"observed_at":"2026-08-15T18:50:34.688401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-06T19:40:00.451887Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05130","last_updated":"2025-07-07T15:42:39Z","snapshot_observed_at":"2026-08-12T05:19:08.709690Z","submitted_at":"2025-07-07T15:42:39Z","title":"Science of the LISA mission: A Summary for the European Strategy for Particle Physics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:40:00.451887Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2507.05130"},"observation_digest":"sha256:b48cd19043341c8ed1a8ba682b04cdc8bff384e63db89d901af7b5bb3c2aed72","observation_id":"a55e7533-df2e-4881-a68a-804b415d4bcd","resolution":{"observed_at":"2026-08-06T19:40:00.451887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-06T15:30:07.315591Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15791","last_updated":"2025-07-21T16:49:54Z","snapshot_observed_at":"2026-08-16T15:37:04.969362Z","submitted_at":"2025-07-21T16:49:54Z","title":"\\texttt{GWBird}: a toolkit for the characterization of the Stochastic Gravitational Wave Background for Ground, Space, and Pulsar Timing Array detectors","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:30:07.315591Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2507.15791"},"observation_digest":"sha256:97aece35e20fd5611feccc0e64e00b16acb8de47bc451d6f550abe517899a62d","observation_id":"ddf7f6dd-5773-4b03-b0cd-457dd24b2306","resolution":{"observed_at":"2026-08-06T15:30:07.315591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-06T12:00:36.018112Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22260","last_updated":"2026-06-05T05:04:01Z","snapshot_observed_at":"2026-08-17T03:23:53.817435Z","submitted_at":"2025-07-29T22:19:33Z","title":"Resonant interactions from dynamical perturbers on generic orbits around an extreme mass ratio inspiral","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:00:36.018112Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2507.22260"},"observation_digest":"sha256:2699597757870bd352866169356c79210017d48730596221383759211c70c134","observation_id":"957583c5-4bc5-4f65-bade-eb07004b0aa8","resolution":{"observed_at":"2026-08-06T12:00:36.018112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-15T16:52:09.566587Z","title":"L., Karnesis, N., Korsakova, N., Gair, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20308","last_updated":"2025-08-27T22:46:30Z","snapshot_observed_at":"2026-08-17T23:04:35.713377Z","submitted_at":"2025-08-27T22:46:30Z","title":"Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T16:52:09.566587Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2508.20308"},"observation_digest":"sha256:80c0c0ffe4c13a01f90ffc1b64b79a6d1894a07a07cbbedf6fbd32c8f3a7c965","observation_id":"4efc5dcc-19cf-4015-b681-af85be0b1dc2","resolution":{"observed_at":"2026-08-15T16:52:09.566587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-04T11:09:30.633456Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.06948","last_updated":"2026-07-14T01:55:02Z","snapshot_observed_at":"2026-08-17T07:44:00.268370Z","submitted_at":"2025-10-08T12:30:56Z","title":"When vacuum breaks: a self-consistency test for astrophysical environments in extreme mass ratio inspirals","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T11:09:30.633456Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2510.06948"},"observation_digest":"sha256:eba9b9f6ced71ce3ede6e078fa3e58fea13b2037442555e69b0c02f5fe318858","observation_id":"2abe45be-968b-421f-95c2-8eb675e2c92b","resolution":{"observed_at":"2026-08-04T11:09:30.633456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2512.16322","last_updated":"2026-03-02T03:56:51Z","snapshot_observed_at":"2026-08-14T12:11:14.435396Z","submitted_at":"2025-12-18T09:03:38Z","title":"First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-16T21:36:50.075797Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2512.16322"},"observation_digest":"sha256:a8d0328190e94ce4e4ed64e95c99480260e6234b11b0692ecbaf67a4d32a66cf","observation_id":"d97ba288-1446-48ee-b125-c4dc7f43ade1","resolution":{"observed_at":"2026-05-16T21:38:34.120025Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2601.23019","last_updated":"2026-05-05T14:01:09Z","snapshot_observed_at":"2026-08-14T10:26:00.239666Z","submitted_at":"2026-01-30T14:27:25Z","title":"Toward claiming a detection of gravitational memory","version":2},"reference_index":197,"source":"pdf_text","source_observed_at":"2026-05-16T09:18:23.702726Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2601.23019"},"observation_digest":"sha256:c01b5331a96570888f0867ffa04be04c5234581414731d27dc64055dae274a43","observation_id":"86450321-6bef-4f2c-8a22-5c45035f5e4b","resolution":{"observed_at":"2026-05-16T09:20:48.052983Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-03T03:09:44.557089Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.09088","last_updated":"2026-07-27T19:13:28Z","snapshot_observed_at":"2026-08-15T11:06:17.800098Z","submitted_at":"2026-02-09T19:00:00Z","title":"Systematic biases in parameter estimation on LISA binaries. II. The effect of excluding higher harmonics for spin-aligned, high-mass binaries","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T03:09:44.557089Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2602.09088"},"observation_digest":"sha256:4c67b10d1eb35382a65fe058e505e94635995cb5f4d7bd2d8719b2b493628e57","observation_id":"74e852c3-0a96-496c-8534-9fcbc067a564","resolution":{"observed_at":"2026-08-03T03:09:44.557089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2602.18560","last_updated":"2026-05-11T13:50:44Z","snapshot_observed_at":"2026-08-16T16:05:45.318396Z","submitted_at":"2026-02-20T19:00:07Z","title":"Inferring the population properties of galactic binaries from LISA's stochastic foreground","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T20:39:31.597021Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2602.18560"},"observation_digest":"sha256:bfefcc97661e73f5c4445d4180349dac46a652dad249fc0b58e5f843b9b8298a","observation_id":"f5367f24-3417-46ac-be9f-4bbabc541f25","resolution":{"observed_at":"2026-05-15T20:40:18.759411Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2604.24330","last_updated":"2026-04-27T11:21:04Z","snapshot_observed_at":"2026-08-11T06:43:12.707302Z","submitted_at":"2026-04-27T11:21:04Z","title":"Pre-localization of Massive Black Hole Binaries in the Millihertz Band","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T02:21:19.211958Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2604.24330"},"observation_digest":"sha256:3f79593805a0f0050dd0ab348d793bdab741c178a479f01b9de52dbc6a7deed7","observation_id":"b32bdd68-55ce-4a10-b74b-626c4b3a8598","resolution":{"observed_at":"2026-05-11T22:51:22.383788Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2605.19121","last_updated":"2026-05-18T21:15:23Z","snapshot_observed_at":"2026-08-16T13:57:12.909629Z","submitted_at":"2026-05-18T21:15:23Z","title":"Ringdown Signatures of Dehnen Dark Matter Halos: Fluid Modes and Detectability with Space-Based Detectors","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-05-20T08:52:43.820655Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2605.19121"},"observation_digest":"sha256:cc9f83c1c1cf1209f97cf049cffbff03a60946fc9c6f576855dec7aeaf75499a","observation_id":"34ffbec5-66d2-4e14-a496-73cc8cd3386a","resolution":{"observed_at":"2026-05-20T08:53:09.684324Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"citing_paper":{"arxiv_id":"2606.21473","last_updated":"2026-07-06T16:41:51Z","snapshot_observed_at":"2026-07-12T13:04:47.322425Z","submitted_at":"2026-06-19T14:23:21Z","title":"The WDM Time-Frequency Transform in Gravitational-Wave Data Analysis I: Formalism","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T13:28:00.209604Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2606.21473"},"observation_digest":"sha256:f032e9c76924ce486e5f7d1095e4fa43ccb042f8a2381c3192b72b014a589995","observation_id":"91517a6b-8820-4c7e-8f13-d84a828cd720","resolution":{"observed_at":"2026-07-04T07:29:38.636889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":"2405.04690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-07-04T07:29:38.635470Z","title":"Efficient GPU-accelerated multisource global fit pipeline for LISA data analysis","venue":null,"work_id":"27b773c9-33cf-4c9f-97f2-1d6980cb6b69","year":2025},"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":27,"source":"pdf_text","source_observed_at":"2026-06-30T08:04:25.273338Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2606.29039"},"observation_digest":"sha256:f429c8c079b801708c26572c29f9835dc552253fd7b0c2a9199e13548750ec62","observation_id":"3866e938-7023-4444-a4ae-d2703218e6b6","resolution":{"observed_at":"2026-06-30T08:14:25.954124Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04690","snapshot_observed_at":"2026-08-01T02:48:49.261221Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25349","last_updated":"2026-07-28T06:54:11Z","snapshot_observed_at":"2026-08-17T19:09:27.362691Z","submitted_at":"2026-07-28T06:54:11Z","title":"Residual Galactic binary foreground in LISA stochastic gravitational-wave background inference: source power concentration and spectral degeneracy","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T02:48:49.261221Z"},"links":{"cited_paper":"/paper/2405.04690","citing_paper":"/paper/2607.25349"},"observation_digest":"sha256:5ec573f2f9b72cce9792728a2c9c2ad3e94f92cc7b67a6606fd138c341ff83a5","observation_id":"0a1d6d5b-ad2f-41aa-8f42-3e76c58c7cc6","resolution":{"observed_at":"2026-08-01T02:48:49.261221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.04690/citation-record","integrity":"/paper/2405.04690/integrity","json":"/paper/2405.04690/citation-record.json","paper":"/paper/2405.04690"},"outbound":[],"paper":{"arxiv_id":"2405.04690","last_updated":"2024-12-11T15:36:46Z","latest_version":2,"primary_category":"gr-qc","snapshot_observed_at":"2026-08-18T04:02:48.094497Z","submitted_at":"2024-05-07T22:04:53Z","title":"An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2405.04690."}