Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Gravitational-Wave Science with Machine Learning
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2005.03745.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:44:03.470815Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T15:21:44.932068Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 9c72b7dc-ddee-4ec0-99ac-790998ae427e · inbound
Adaptive cancellation of mains power interference in continuous gravitational wave searches with a hidden Markov model cites this paper.
Adaptive cancellation of mains power interference in continuous gravitational wave searches with a hidden Markov model Enhancing Gravitational-Wave Science with Machine Learning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T04:49:37.900152Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea305fe3-c577-4432-a77c-59a0885972af · inbound
Applications of machine learning in gravitational wave research with current interferometric detectors cites this paper.
Applications of machine learning in gravitational wave research with current interferometric detectors Enhancing Gravitational-Wave Science with Machine Learning
Reference 141
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:42:50.118174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77d5ce0b-92e0-4aaa-b9aa-49f8156c561c · inbound
Approximating neutron-star radii using gravitational-wave only measurements with symbolic regression cites this paper.
Approximating neutron-star radii using gravitational-wave only measurements with symbolic regression Enhancing Gravitational-Wave Science with Machine Learning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:44:03.470815Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b4ea3ca-dcf8-43f5-a1fb-6d91371b5455 · inbound
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data cites this paper.
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data Enhancing Gravitational-Wave Science with Machine Learning
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T15:21:44.935034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0c2c4601-b414-412d-aa9a-148fc5668928 · inbound
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data cites this paper.
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data Enhancing Gravitational-Wave Science with Machine Learning
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:10:01.615711Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41dd1d33-3e24-4151-aa5b-417e462a390d · inbound
Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework cites this paper.
Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework Enhancing Gravitational-Wave Science with Machine Learning
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T06:10:38.314004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8e13543c-45a2-44b6-8b16-9afd132fbc06 · inbound
Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection cites this paper.
Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection Enhancing Gravitational-Wave Science with Machine Learning
Reference 123
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T03:33:56.198340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1da35553-f9c1-4b25-ac7e-9a9ab11406e0 · inbound
Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.
Predicting intermediate-mass black hole formation in star clusters with machine learning Enhancing Gravitational-Wave Science with Machine Learning
Reference 96
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-22T09:21:21.233041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c3c0c88c-7954-449f-9566-9af8b9db5167 · inbound
Probability of gravitational-wave lensing by intermediate-mass black holes and globular clusters cites this paper.
Probability of gravitational-wave lensing by intermediate-mass black holes and globular clusters Enhancing Gravitational-Wave Science with Machine Learning
Reference 92
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:33:04.577974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.