Paper Citation Record · LEDGER

Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.11366.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2306.11366 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:54:52.018461Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T03:33:56.071768Z

Reference resolution

0 of 0 outbound references displayed

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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 f413eb1c-ebea-466a-b905-4b0ebaf7f3c7 · inbound

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run cites this paper.

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:54:52.018461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c46419a6-96f0-4512-aa1c-48d7538440a5 · inbound

Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation cites this paper.

Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:29:57.130309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22b38285-dbf0-4ff1-bc51-e52e9eafb548 · inbound

Interpretable Analytic Calabi-Yau Metrics via Symbolic Distillation cites this paper.

Interpretable Analytic Calabi-Yau Metrics via Symbolic Distillation Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:22.376869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0993e2a9-092c-40d8-b899-ccd6792aaa6d · 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 Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:33:56.072991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.