Paper Citation Record · LEDGER

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.09888.

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

pith.paper-citation-record.v1
2412.09888 v4

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:41:07.169345Z

measured 37 of 37 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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verified fuzzy8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7074d0e0-2011-4dd3-b15a-4c3cdb8c7ccf · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.971366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dd07d1c8-85c7-4af9-b778-85cad3028e4c · outbound

This paper cites ASTROPHYSICAL IMPLICATIONS OF THE BINARY BLACK HOLE MERGER GW150914.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves ASTROPHYSICAL IMPLICATIONS OF THE BINARY BLACK HOLE MERGER GW150914

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.973008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93b6d43d-f3f8-4d9c-ac54-a0fa6a19f2ba · outbound

This paper cites GW150914: First Results from the Search for Binary Black Hole Coalescence with Advanced LIGO.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves GW150914: First Results from the Search for Binary Black Hole Coalescence with Advanced LIGO

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T16:41:07.955022Z

Source-reported events for the cited work

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Observation 2ed85b64-d6d4-4753-9bff-9354ee696c21 · outbound

This paper cites Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.983419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee3b54cc-ca4d-4d5c-9b5e-9e29991c0994 · outbound

This paper cites Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.988238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9608430f-6465-46cb-b8b0-a3764df7b95a · outbound

This paper cites Search for Lensing Signatures in the Gravitational-Wave Observations from the First Half of LIGO-Virgo’s Third Observing Run.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Search for Lensing Signatures in the Gravitational-Wave Observations from the First Half of LIGO-Virgo’s Third Observing Run

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.993443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24c57d07-e407-467e-8b43-36b49561e282 · outbound

This paper cites Increasing the Astrophysical Reach of the Advanced Virgo Detector via the Application of Squeezed Vacuum States of Light.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Increasing the Astrophysical Reach of the Advanced Virgo Detector via the Application of Squeezed Vacuum States of Light

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.999035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0174ff19-2162-4039-a8ec-11d0efcad0d0 · outbound

This paper cites Advanced Virgo: A Second-Generation Interferometric Gravitational Wave Detector.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Advanced Virgo: A Second-Generation Interferometric Gravitational Wave Detector

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T16:41:07.938505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bbd38fe4-f6b3-4906-be42-e2c53133d9e6 · outbound

This paper cites Overview of KAGRA: Detector Design and Construction History.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Overview of KAGRA: Detector Design and Construction History

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.008822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8c0d562-d297-4644-aa0d-d0c2f5cf6ef2 · outbound

This paper cites FINDCHIRP: An Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves FINDCHIRP: An Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries

Reference 10

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T16:41:07.920769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1ea505b-f148-4477-8d15-35355ed60280 · outbound

This paper cites an unresolved cited work.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.018567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39bcdc9f-30b0-47a3-b6b2-9f52365bd453 · outbound

This paper cites Sensitivity and Performance of the Advanced LIGO Detectors in the Third Observing Run.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Sensitivity and Performance of the Advanced LIGO Detectors in the Third Observing Run

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.880686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fcebeed4-faf1-4407-a9bc-9a1ef08139a1 · outbound

This paper cites Neural Network Emulator of the Advanced LIGO and Advanced Virgo Selection Function.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Neural Network Emulator of the Advanced LIGO and Advanced Virgo Selection Function

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.861412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 487e670c-abfb-4d0d-b7eb-583a3fe88aa6 · outbound

This paper cites Rapid Determination of LISA Sensitivity to Extreme Mass Ratio Inspirals with Machine Learning.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Rapid Determination of LISA Sensitivity to Extreme Mass Ratio Inspirals with Machine Learning

Reference 14

Resolution: verified exact; doi, observed 2026-08-11T16:41:07.325220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec14da3c-114a-42c0-b027-6a3a25bb3db5 · outbound

This paper cites Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.039328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f7abf2-72af-417b-826e-463caff7f03b · outbound

This paper cites Differentiable and hardware-accelerated waveforms for gravitational wave data analysis.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Differentiable and hardware-accelerated waveforms for gravitational wave data analysis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.045129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19d4c56e-3dda-4d9d-a1e5-2733dfa249db · outbound

This paper cites Semianalytic Sensitivity Estimates for Catalogs of Gravitational-Wave Transients.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Semianalytic Sensitivity Estimates for Catalogs of Gravitational-Wave Transients

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.049916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e70f892-17cb-4204-92b1-9c765d26eafe · outbound

This paper cites Ensuring Consistency Between Noise and Detection in Hierarchical Bayesian Inference.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Ensuring Consistency Between Noise and Detection in Hierarchical Bayesian Inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.055086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde69ee4-23df-4056-a185-0fbea57952ce · outbound

This paper cites The Most Massive Binary Black Hole Detections and the Identification of Population Outliers.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves The Most Massive Binary Black Hole Detections and the Identification of Population Outliers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.059816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6e94c37-db84-4894-9eec-3679d5bc2f8e · outbound

This paper cites Gravitational-Wave Selection Effects Using Neural-Network Classifiers.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Gravitational-Wave Selection Effects Using Neural-Network Classifiers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.064670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974bf66b-7118-442e-bc82-b6e6e2e48a13 · outbound

This paper cites an unresolved cited work.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-11T16:41:07.842471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fb7571c1-f710-4aa4-b926-919051659448 · outbound

This paper cites JAX: Composable Transformations of Python+NumPy Programs.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves JAX: Composable Transformations of Python+NumPy Programs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.822782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5938c19a-d7af-487e-ae0b-c09fe4fc0133 · outbound

This paper cites Follow-up analyses to the O3 LIGO-Virgo-KAGRA lensing searches.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Follow-up analyses to the O3 LIGO-Virgo-KAGRA lensing searches

Reference 23

Resolution: malformed identifier; no resolver link, observed 2026-08-11T16:41:07.082436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f563eba9-6eb6-4e2f-abeb-683c7be7f29a · outbound

This paper cites Numba: A High Performance Python Compiler.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Numba: A High Performance Python Compiler

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.801638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5833f84d-2f55-427e-b0a4-d6004a9c016e · outbound

This paper cites LVK Algorithm Library - LALSuite.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves LVK Algorithm Library - LALSuite

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T16:41:07.781968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e3f1f397-6209-4e5f-8ae0-4b4aa9243d2a · outbound

This paper cites Gravitational lensing: towards combining the multi-messengers.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Gravitational lensing: towards combining the multi-messengers

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:41:07.495405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c5186d9a-1687-4f41-93e6-78f8c6747242 · outbound

This paper cites Uncovering faint lensed gravitational-wave signals and reprioritizing their follow-up analysis using galaxy lensing forecasts with detected counterparts.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Uncovering faint lensed gravitational-wave signals and reprioritizing their follow-up analysis using galaxy lensing forecasts with detected counterparts

Reference 27

Resolution: malformed identifier; local_arxiv, observed 2026-08-11T16:41:07.461662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 30dc00bf-8162-4c20-94e0-571186888c76 · outbound

This paper cites NumPy: A Fundamental Package for Scientific Computing with Python.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves NumPy: A Fundamental Package for Scientific Computing with Python

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.763849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 54120851-12a9-49c8-9215-84b5f83ca52b · outbound

This paper cites Scikit-Learn: Machine Learning in Python.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Scikit-Learn: Machine Learning in Python

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.743873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 164b1ef6-3cfa-4694-8e13-12ab69ed8e0a · outbound

This paper cites an unresolved cited work.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-11T16:41:07.724797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd746445-9bbc-4a83-8d60-50d82be8707f · outbound

This paper cites Mining Gravitational-Wave Catalogs to Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Mining Gravitational-Wave Catalogs to Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.141229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 832a1523-2c66-4419-9a29-a966afd4edb5 · outbound

This paper cites Advanced LIGO.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Advanced LIGO

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.147476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbf4a693-8f3c-46d8-a9e9-0451bac566dc · outbound

This paper cites An Introduction to Bayesian Inference in Gravitational-Wave Astronomy: Parameter Estimation, Model Selection, and Hier- archical Models.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves An Introduction to Bayesian Inference in Gravitational-Wave Astronomy: Parameter Estimation, Model Selection, and Hier- archical Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.153157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0af8e590-c586-474b-b78a-6cf01a9e0d01 · outbound

This paper cites SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:07.705081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a6f66290-20e0-40b3-8bf8-3a1790065b5a · outbound

This paper cites On the detection and precise localisation of merging black holes events through strong gravitational lensing.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves On the detection and precise localisation of merging black holes events through strong gravitational lensing

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.163052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 691b5e76-2496-4b3f-92f1-a9346da08783 · outbound

This paper cites Beyond the Detector Horizon: Forecasting Gravitational-Wave Strong Lensing.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves Beyond the Detector Horizon: Forecasting Gravitational-Wave Strong Lensing

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.169345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 027c7ddb-b7f7-4cc6-a2fc-15b0a8bcab7c · outbound

This paper cites ler: LVK (LIGO-Virgo-KAGRA collaboration) event (compact-binary mergers) rate calculator and simulator.

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves ler: LVK (LIGO-Virgo-KAGRA collaboration) event (compact-binary mergers) rate calculator and simulator

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:07.134278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.