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Paper Citation Record · LEDGER

Noise Reduction in Gravitational-wave Data via Deep Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2005.06534.

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

pith.paper-citation-record.v1
2005.06534 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:49:37.911065Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T03:33:56.402172Z

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 29a240d7-0d07-423a-92c6-7aaaea81afda · inbound

Adaptive cancellation of mains power interference in continuous gravitational wave searches with a hidden Markov model cites this paper.

Adaptive cancellation of mains power interference in continuous gravitational wave searches with a hidden Markov model Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-12T04:49:37.911065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:49:37.911065Z digest=sha256:7aa6e5e3fe48683ec72c5f0fa853a25eea3ea859c3303dc10670f5fe50b7ee41

Observation 9ee39c63-62e4-4231-b853-e7af5c677b80 · 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 Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-10T23:54:52.009729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:54:52.009729Z digest=sha256:109524f40cf36da031a6ecd735b9d0be5ad4062789156db0ae01f4b7593cdf1b

Observation 650be4f9-b590-4998-bfb7-1dc735f31432 · inbound

Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis cites this paper.

Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T17:36:41.374503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-18T17:33:37.594218Z digest=sha256:2ca6ee2ec64e52485e1cfcf88da36e615e306dfff17196a1e31984bbd280f1da

Observation b85e3577-a99f-4141-aa75-1495a79be484 · 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 Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:10:22.865016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-17T22:06:32.129237Z digest=sha256:c231445332c75da77480eaf8d4eb4604da3d3675c979878aa387db752dc92f73

Observation 48d378b2-f2da-4103-96a2-455ef8dbee6a · 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 Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 125

Resolution
verified exact
arxiv_id, observed 2026-05-21T03:33:56.403631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-21T03:33:53.198336Z digest=sha256:b5124ce772c61fe7be3b062acd6e75722e3f38fde84e9b704f231e5aff2ecc3f