Paper Citation Record · LEDGER

A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2403.18661.

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

pith.paper-citation-record.v1
2403.18661 v2

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:02:38.885003Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:54.899742Z

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 748ab7c6-c56d-4395-baec-3b22cf674796 · inbound

Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction cites this paper.

Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T14:40:16.095951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1159f08b-9ffd-4064-855f-03993f9b3608 · 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 A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 280

Resolution: unresolved; no resolver link, observed 2026-08-11T11:42:50.821196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86b6a5b7-a444-4f7b-a2e4-62d6d7978821 · 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 A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:54:51.998572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8e3b68d-1651-4de8-b97f-182de984cd34 · inbound

Pre-trained Audio Transformer as a Foundational AI Tool for Gravitational Waves cites this paper.

Pre-trained Audio Transformer as a Foundational AI Tool for Gravitational Waves A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:32.381704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23bf3c74-1f8d-4d20-bc3b-dd685e004a83 · inbound

Improving the detection significance of gravitational wave transient searches with CNN models cites this paper.

Improving the detection significance of gravitational wave transient searches with CNN models A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T22:02:38.885003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d68c5e93-e6eb-4ee6-bfea-7555300f0706 · 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 A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T18:29:57.675480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5f262d-e7fd-4713-8570-cf9a0d5dd3fa · inbound

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms cites this paper.

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-05T05:30:31.385331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a765da-2bcf-47d4-83d3-7d4aa7dda808 · inbound

A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences cites this paper.

A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:16:36.028097Z

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 5dd282ce-af4b-4f18-b96a-39f5ae67b7b0 · inbound

Auto-encoder model for faster generation of effective one-body gravitational waveform approximations cites this paper.

Auto-encoder model for faster generation of effective one-body gravitational waveform approximations A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:10:22.848988Z

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 9250362a-754b-441a-b77e-165f1bd0bd35 · inbound

Flexible Gravitational-Wave Parameter Estimation with Transformers cites this paper.

Flexible Gravitational-Wave Parameter Estimation with Transformers A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T18:59:09.713742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fc5ac1a-9f38-442d-9afd-b1f062510c5f · inbound

Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run cites this paper.

Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T18:39:17.271627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8770235-c7ea-4d54-aa8d-6624bee87160 · inbound

Beyond FINDCHIRP: Breaking the memory wall and optimal FFTs for Gravitational-Wave Matched-Filter Searches with Ratio-Filter Dechirping cites this paper.

Beyond FINDCHIRP: Breaking the memory wall and optimal FFTs for Gravitational-Wave Matched-Filter Searches with Ratio-Filter Dechirping A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T11:30:52.869882Z

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 de47c732-a192-4c0e-9464-a1e7e2a754e6 · inbound

AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity cites this paper.

AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:58:54.901193Z

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.