Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.19003.
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 2026-08-15T19:11:25.222031Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T03:33:56.341829Z
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 5e7dc6e5-2aa4-4686-818e-d03e80bb01e7 · inbound
Parameter inference of millilensed gravitational waves using neural spline flows cites this paper.
Parameter inference of millilensed gravitational waves using neural spline flows Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 80
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T13:22:18.532394Z
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 1f750343-5b59-4384-b9a4-441ba1bf5e1e · inbound
Black Hole Spectroscopy with Conditional Variational Autoencoder cites this paper.
Black Hole Spectroscopy with Conditional Variational Autoencoder Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:11:25.222031Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 780f36db-7cd1-4414-b4e5-e2f3f4233dfc · inbound
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms cites this paper.
Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:30:31.333922Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d5b87bb-4a61-47f3-b515-db3b79b856ea · inbound
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows cites this paper.
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 90
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T12:50:25.805463Z
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 0b63ef22-2e61-4315-bf54-d6c90919f804 · 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 Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 131
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T03:33:56.343428Z
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 4a24a909-6bc3-4102-820c-c08c507ce897 · inbound
Identifying lensed gravitational waves with physics-informed posterior learning cites this paper.
Identifying lensed gravitational waves with physics-informed posterior learning Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
Reference 130
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T23:16:00.672720Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.