Paper Citation Record · LEDGER

Learning Bayesian posteriors with neural networks for gravitational-wave inference

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1909.05966.

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

pith.paper-citation-record.v1
1909.05966 v3

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measured 0 of 0 reference resolution

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measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:33:04.582520Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T15:21:44.920570Z

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Outbound references

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Pith citing papers

Observation 112c05aa-3db4-48f5-861a-b9b24c9086cc · 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 Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:21:44.923765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3adbf248-fadc-47bf-816d-0b8339263ed2 · inbound

Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning cites this paper.

Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T10:35:01.805500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c6c8df-2e2f-43e5-a235-e57474336ec3 · inbound

Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses cites this paper.

Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T23:47:05.923559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2433a3bc-0fae-4ee3-8cdc-855c263ff8fd · inbound

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

Flexible Gravitational-Wave Parameter Estimation with Transformers Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T18:59:10.234103Z

Source-reported events for the cited work

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Observation 33e862af-fc32-4599-8197-53b01b9d95d1 · inbound

The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference cites this paper.

The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T12:37:09.667105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12330ca4-0afa-48bc-af51-4b74b8e4309d · inbound

Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries cites this paper.

Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:46:32.008343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 93caf485-5f09-410f-a325-e9f75bbb8110 · inbound

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms cites this paper.

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-31T04:58:16.800792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5dd65d7-4b38-41d0-bc12-c71e0b78d4b3 · 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 Learning Bayesian posteriors with neural networks for gravitational-wave inference

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-11T00:33:04.582520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.