Paper Citation Record · LEDGER

Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.08429.

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

pith.paper-citation-record.v1
2308.08429 v4

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 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:54:51.994283Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T05:30:32.295291Z

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 713bd39a-18f1-478e-8b1a-6630eeeb6cff · 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 Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T23:54:51.994283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70b8caa8-70f8-4ac9-8f85-9b250c8c035b · inbound

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis cites this paper.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

Reference 142

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:24.871732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdcec253-bd1a-40be-b9fb-6a6cc4aa1bf6 · inbound

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

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:30:32.301458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation df60a342-e886-4679-9874-18a7629b0776 · 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 Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T18:39:17.707235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.