Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Exploring Supernova Gravitational Waves with Machine Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2209.14542.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T23:55:23.354251Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T03:33:56.458872Z
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 ff6baa37-62bb-42c3-9870-994ac00e6ad9 · inbound
Impact of rotation on the accretion of entropy perturbations in collapsing massive stars cites this paper.
Impact of rotation on the accretion of entropy perturbations in collapsing massive stars Exploring Supernova Gravitational Waves with Machine Learning
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:19:17.000572Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88239a0c-24b2-4f68-837f-7281caa2782b · inbound
Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves cites this paper.
Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves Exploring Supernova Gravitational Waves with Machine Learning
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T16:48:58.103396Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9d9397d-2211-4190-8d2c-ad23257162ca · inbound
Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves cites this paper.
Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves Exploring Supernova Gravitational Waves with Machine Learning
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T05:43:31.579074Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b3f5b3f-e726-4795-b706-6806e7d6cf0e · inbound
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors cites this paper.
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors Exploring Supernova Gravitational Waves with Machine Learning
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:26:11.635656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cee89bc4-f06d-4584-a2ed-71df9024eaf4 · 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 Exploring Supernova Gravitational Waves with Machine Learning
Reference 109
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T03:33:56.460257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 12e6c9e3-699a-4d0c-b631-101d0f751ba9 · inbound
Parameter Estimation Horizon of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors cites this paper.
Parameter Estimation Horizon of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors Exploring Supernova Gravitational Waves with Machine Learning
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T23:55:23.354251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.