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arxiv: 2606.31304 · v1 · pith:RBPKNXHInew · submitted 2026-06-30 · 🌀 gr-qc · astro-ph.HE· astro-ph.IM

A parametric signal plus noise inference framework for short duration non-Gaussian noise transients

Charlie Hoy , Ruxandra Bondarescu , Andrew Lundgren , Laura K. Nuttall This is my paper

Pith reviewed 2026-07-01 04:34 UTC · model grok-4.3

classification 🌀 gr-qc astro-ph.HEastro-ph.IM
keywords gravitational wavesBayesian inferenceglitchesnon-Gaussian noiseparameter estimationLIGOnoise transients
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The pith

A joint signal-plus-glitch model recovers the true source properties of gravitational waves even when loud noise transients overlap the signal.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a Bayesian inference method that simultaneously fits an astrophysical gravitational wave signal and a parametric model for short-duration non-Gaussian noise artifacts called glitches. Standard analyses assume the detector noise is stationary and Gaussian, an assumption that breaks when glitches overlap signals and produces biased estimates of source parameters. By adding the glitch model as additional parameters to be inferred alongside the signal, the method recovers the injected source properties in simulations where traditional approaches fail. The same joint modeling also avoids spurious claims that general relativity has been violated due to glitch contamination. This matters because real detector data routinely contains such transients that can mimic or distort signals from black hole or neutron star mergers.

Core claim

By extending the standard Bayesian parameter estimation framework to include a quasi-physical glitch model for short-duration non-Gaussian transients, the analysis infers the true source properties of simulated gravitational wave signals contaminated by loud glitches, whereas conventional techniques that treat the noise as Gaussian produce biased results. The method also prevents false claims of general relativity violations and confirms the exceptional character of the signals in contaminated data.

What carries the argument

The bilby-antiglitch framework, which augments the signal model with a parametric quasi-physical description of short non-Gaussian noise transients that are sampled jointly with the astrophysical parameters.

If this is right

  • Source parameters for signals overlapped by loud glitches are recovered without bias.
  • False indications that general relativity is violated due to glitch effects are avoided.
  • The exceptional nature of genuine gravitational wave signals can be validated even in contaminated data segments.
  • Inference remains reliable in realistic detector data that contains short non-Gaussian transients.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same joint modeling approach could be extended to longer-duration or other classes of noise artifacts that currently require separate vetoes.
  • Testing on real events where independent evidence confirms the presence of a glitch would show whether the method changes published parameter estimates.
  • If the glitch model generalizes across detectors, it could reduce reliance on aggressive data cleaning steps before analysis.
  • The framework suggests that explicit noise modeling may become routine rather than an exception in future gravitational wave catalogs.

Load-bearing premise

The chosen quasi-physical glitch model must be flexible enough to describe the actual statistical properties of real noise transients without adding its own systematic biases to the recovered signal parameters.

What would settle it

Apply the method to a set of simulated signals with known true parameters injected into real LIGO data segments that contain verified glitches; if the recovered posterior distributions remain centered on the true values while standard analyses remain biased, the claim holds, otherwise it is falsified.

Figures

Figures reproduced from arXiv: 2606.31304 by Andrew Lundgren, Charlie Hoy, Laura K. Nuttall, Ruxandra Bondarescu.

Figure 1
Figure 1. Figure 1: The spectrogram of a simulated GW signal in the pres￾ence of non-Gaussian noise, which was used to verify our signal plus noise inference pipeline. The dashed line shows the frequency track of the simulated GW150914-like signal, and the solid box isolates the blip glitch 0.2 seconds before the merger. model G, such that m = M + G, we recover the fundamental assumption in the Whittle likelihood: any non-Gau… view at source ↗
Figure 2
Figure 2. Figure 2: The inferred posterior distribution of a simulated signal in non-Gaussian noise. The Top Left and Top right panels show the inferred component masses and effective aligned-spin obtained under the signal plus noise bilby-antiglitch and signal-only bilby hypotheses respectively. The vertical and horizontal crosshairs show the true values, and the contours show the 90% credible intervals. Due to significant b… view at source ↗
Figure 4
Figure 4. Figure 4: The inferred component masses when analysing a sim￾ulated signal in non-Gaussian noise. We compare the inferred dis￾tributions from our joint signal plus noise analysis with bilby￾antiglitch, and from a signal-only analysis with bilby after the median realisation of the glitch inferred with bilby-antiglitch is subtracted. We also show the variation in the inferred component masses when performing a signal-… view at source ↗
Figure 5
Figure 5. Figure 5: The reconstructed astrophysical and glitch signals inferred by bilby-antiglitch when analysing GW250114 082203. For simplicity we only show the glitch and astrophysical CBC signal projected into LIGO-Livingston. For comparison we show the whitened strain data. The reconstructed GW signals are plotted as a band representing the 90% credible interval. rather than the underlying GW signal, we expect signifi￾c… view at source ↗
Figure 6
Figure 6. Figure 6: The two-dimensional posterior distribution for the spin of the primary black hole when analysing GW200129 065458 with bilby-antiglitch. Tilt angles of 90◦ means that the spin vector lies within the plane of the binary. The colour indicates the poste￾rior probability per pixel. This plot is produced by using histogram bins that are constructed linearly in spin magnitude and the co￾sine of the tilt angles su… view at source ↗
read the original abstract

Gravitational waves are now routinely detected with ground-based observatories, and, through a process known as Bayesian inference, their source properties are inferred. However, terrestrial noise artifacts, often referred to as glitches, commonly overlap astrophysical signals. This invalidates a fundamental assumption of gravitational wave analyses: the noise is no longer stationary and Gaussian. As a result, traditional techniques can provide biased inferences in realistic data. One method for mitigating the effect of glitches is to jointly analyse both the signal and noise in a single framework. In this work, we introduce bilby-antiglitch to infer the astrophysical signal properties in non-Gaussian noise. By additionally including a quasi-physical glitch model to describe short duration non-Gaussian noise transients, we show that unlike traditional techniques, we infer the true source properties of simulated signals contaminated with loud glitches. We also show that bilby-antiglitch prevents false violation claims of General Relativity, and validates the exceptional nature of gravitational wave signals in spurious data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces bilby-antiglitch, a Bayesian inference framework extending bilby to jointly model astrophysical gravitational-wave signals and short-duration non-Gaussian noise transients via a quasi-physical parametric glitch model. It claims that, unlike standard analyses assuming stationary Gaussian noise, this approach recovers the true source parameters from simulated signals contaminated by loud glitches and prevents false claims of general-relativity violations.

Significance. If the quasi-physical glitch model generalizes without introducing bias, the framework addresses a recurring practical problem in LIGO/Virgo analyses where glitches overlap signals. The joint signal-plus-noise modeling and software implementation represent a constructive contribution to reproducible data-analysis methods in the field.

major comments (2)
  1. [Abstract] Abstract: the central claim that true source properties are recovered rests on simulations of signals plus loud glitches, yet supplies no quantitative metrics (bias, credible-interval coverage, or recovery fractions) or error budgets, preventing assessment of whether the improvement over traditional techniques is statistically meaningful.
  2. [Abstract] Abstract and results sections: the validation uses simulated glitches whose statistical properties are generated from the same quasi-physical parametric family employed in the inference; this leaves open whether recovery would hold for real non-Gaussian transients whose morphology deviates from the model, directly undermining the claim of robustness against realistic data.
minor comments (1)
  1. Notation for the glitch model parameters is introduced without an explicit equation or table summarizing the functional form and priors; this should be added for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed report. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that true source properties are recovered rests on simulations of signals plus loud glitches, yet supplies no quantitative metrics (bias, credible-interval coverage, or recovery fractions) or error budgets, preventing assessment of whether the improvement over traditional techniques is statistically meaningful.

    Authors: We agree that the abstract would benefit from explicit quantitative metrics to support the central claim. The manuscript demonstrates parameter recovery primarily through posterior plots and qualitative comparison to standard analyses, but does not report numerical bias values, coverage fractions, or formal error budgets. In the revised version we will add these metrics (e.g., median bias and 68% credible-interval coverage across an ensemble of injections) both in the abstract and in a new results subsection. revision: yes

  2. Referee: [Abstract] Abstract and results sections: the validation uses simulated glitches whose statistical properties are generated from the same quasi-physical parametric family employed in the inference; this leaves open whether recovery would hold for real non-Gaussian transients whose morphology deviates from the model, directly undermining the claim of robustness against realistic data.

    Authors: This is a substantive limitation of the current validation. The simulations are generated from the same parametric family to establish that the joint inference is unbiased when the glitch model is correctly specified. We will expand the discussion section to explicitly acknowledge the risk of model mismatch for real glitches, include a brief exploration of injected glitches with deliberately altered morphology (e.g., different rise-time or frequency content), and qualify the robustness claims accordingly. Full validation against real LIGO glitches lies beyond the scope of this work and will be noted as future research. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a new Bayesian inference framework (bilby-antiglitch) that augments standard signal models with an additional quasi-physical glitch model for non-Gaussian transients. All reported results concern recovery of injected signals in simulated data; no derivation chain, fitted parameters, or self-citation is shown that reduces a claimed prediction to an input by construction. The central performance claim is an empirical demonstration on external simulations rather than a self-referential identity, satisfying the default expectation of a non-circular analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities can be extracted. The 'quasi-physical glitch model' is mentioned but its functional form, number of parameters, and assumptions are not stated.

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discussion (0)

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Reference graph

Works this paper leans on

107 extracted references · 90 canonical work pages · 34 internal anchors

  1. [1]

    Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data

    Hourihane, Sophie and Chatziioannou, Katerina and Wijngaarden, Marcella and Davis, Derek and Littenberg, Tyson and Cornish, Neil. Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data. Phys. Rev. D. 2022. doi:10.1103/PhysRevD.106.042006. arXiv:2205.13580

    work page doi:10.1103/physrevd.106.042006 2022

  2. [2]

    Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors

    Ashton, Gregory and Thiele, Sarah and Lecoeuche, Yannick and McIver, Jess and Nuttall, Laura K. Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors. Class. Quant. Grav. 2022. doi:10.1088/1361-6382/ac8094. arXiv:2110.02689

    work page doi:10.1088/1361-6382/ac8094 2022

  3. [3]

    Detecting gravitational wave signals using a flexible model for the amplitude and frequency evolution

    Gupta, Toral and Cornish, Neil J. Detecting gravitational wave signals using a flexible model for the amplitude and frequency evolution. Phys. Rev. D. 2024. doi:10.1103/PhysRevD.110.064053. arXiv:2404.11719

    work page doi:10.1103/physrevd.110.064053 2024

  4. [4]

    and Littenberg, Tyson B

    Cornish, Neil J. and Littenberg, Tyson B. and B \'e csy, Bence and Chatziioannou, Katerina and Clark, James A. and Ghonge, Sudarshan and Millhouse, Margaret. BayesWave analysis pipeline in the era of gravitational wave observations. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.103.044006. arXiv:2011.09494

    work page doi:10.1103/physrevd.103.044006 2021

  5. [5]

    Enabling high confidence detections of gravitational-wave bursts

    Littenberg, Tyson B. and Kanner, Jonah B. and Cornish, Neil J. and Millhouse, Margaret. Enabling high confidence detections of gravitational-wave bursts. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.94.044050. arXiv:1511.08752

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.94.044050 2016

  6. [6]

    BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches

    Cornish, Neil J. and Littenberg, Tyson B. BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches. Class. Quant. Grav. 2015. doi:10.1088/0264-9381/32/13/135012. arXiv:1410.3835

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/0264-9381/32/13/135012 2015

  7. [7]

    Gaussian processes for glitch-robust gravitational-wave astronomy

    Ashton, Gregory. Gaussian processes for glitch-robust gravitational-wave astronomy. Mon. Not. Roy. Astron. Soc. 2023. doi:10.1093/mnras/stad341. arXiv:2209.15547

    work page doi:10.1093/mnras/stad341 2023

  8. [8]

    Remnant of binary black-hole mergers: New simulations and peak luminosity studies

    Healy, James and Lousto, Carlos O. Remnant of binary black-hole mergers: New simulations and peak luminosity studies. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.024037. arXiv:1610.09713

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.95.024037 2017

  9. [9]

    Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

    Veitch, J. and others. Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.91.042003. arXiv:1409.7215

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.91.042003 2015

  10. [10]

    Abbott, B. P. and others. Observation of Gravitational Waves from a Binary Black Hole Merger. Phys. Rev. Lett. 2016. doi:10.1103/PhysRevLett.116.061102. arXiv:1602.03837

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevlett.116.061102 2016

  11. [11]

    Abbott, B. P. and others. Properties of the Binary Black Hole Merger GW150914. Phys. Rev. Lett. 2016. doi:10.1103/PhysRevLett.116.241102. arXiv:1602.03840

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevlett.116.241102 2016

  12. [12]

    Towards models of gravitational waveforms from generic binaries II: Modelling precession effects with a single effective precession parameter

    Schmidt, Patricia and Ohme, Frank and Hannam, Mark. Towards models of gravitational waveforms from generic binaries II: Modelling precession effects with a single effective precession parameter. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.91.024043. arXiv:1408.1810

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.91.024043 2015

  13. [13]

    and Karamanis, Minas and Luo, Yilin and Seljak, Uro s

    Williams, Michael J. and Karamanis, Minas and Luo, Yilin and Seljak, Uro s. Validating sequential Monte Carlo for gravitational-wave inference. Mon. Not. Roy. Astron. Soc. 2025. doi:10.1093/mnras/staf1458. arXiv:2506.18977

    work page doi:10.1093/mnras/staf1458 2025

  14. [14]

    Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model

    Ashton, Gregory and Talbot, Colm and Lundgren, Andrew and Malz, Ann-Kristin and Areeda, Joseph. Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model. arXiv:2604.16039. 2026. arXiv:2604.16039

    Pith/arXiv arXiv 2026

  15. [15]

    Inspiral-merger-ringdown waveforms for black-hole binaries with non-precessing spins

    Ajith, P. and others. Inspiral-merger-ringdown waveforms for black-hole binaries with non-precessing spins. Phys. Rev. Lett. 2011. doi:10.1103/PhysRevLett.106.241101. arXiv:0909.2867

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevlett.106.241101 2011

  16. [16]

    and Nuttall, L

    Mozzon, S. and Nuttall, L. K. and Lundgren, A. and Dent, T. and Kumar, S. and Nitz, A. H. Dynamic Normalization for Compact Binary Coalescence Searches in Non-Stationary Noise. Class. Quant. Grav. 2020. doi:10.1088/1361-6382/abac6c. arXiv:2002.09407

    work page doi:10.1088/1361-6382/abac6c 2020

  17. [17]

    Journal of the American statistical association , volume=

    The monte carlo method , author=. Journal of the American statistical association , volume=. 1949 , publisher=

    1949

  18. [18]

    Abac, A. G. and others. GW250114: Testing Hawking s Area Law and the Kerr Nature of Black Holes. Phys. Rev. Lett. 2025. doi:10.1103/kw5g-d732. arXiv:2509.08054

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/kw5g-d732 2025

  19. [19]

    Abac, A. G. and others. GW241011 and GW241110: Exploring Binary Formation and Fundamental Physics with Asymmetric, High-spin Black Hole Coalescences. Astrophys. J. Lett. 2025. doi:10.3847/2041-8213/ae0d54. arXiv:2510.26931

    work page doi:10.3847/2041-8213/ae0d54 2025

  20. [20]

    Incorporation of model accuracy in gravitational wave Bayesian inference

    Hoy, Charlie and Akcay, Sarp and Mac Uilliam, Jake and Thompson, Jonathan E. Incorporation of model accuracy in gravitational wave Bayesian inference. Nature Astron. 2025. doi:10.1038/s41550-025-02579-7. arXiv:2409.19404

    work page doi:10.1038/s41550-025-02579-7 2025

  21. [21]

    Leveraging rapid parameter estimates for efficient gravitational-wave Bayesian inference via posterior repartitioning

    Prathaban, Metha and Hoy, Charlie and Williams, Michael J. Leveraging rapid parameter estimates for efficient gravitational-wave Bayesian inference via posterior repartitioning. arXiv:2601.21630. 2026. arXiv:2601.21630

    arXiv 2026

  22. [22]

    Joint inference for gravitational wave signals and glitches using a data-informed glitch model

    Malz, Ann-Kristin and Veitch, John. Joint inference for gravitational wave signals and glitches using a data-informed glitch model. Phys. Rev. D. 2025. doi:10.1103/fp4b-mvzx. arXiv:2505.00657

    work page doi:10.1103/fp4b-mvzx 2025

  23. [23]

    GWTC-5.0: Population Properties of Merging Compact Binaries

    The LIGO Scientific, Virgo and KAGRA collaboration. GWTC-5.0: Population Properties of Merging Compact Binaries. arXiv:2605.27226. 2026. arXiv:2605.27226

    Pith/arXiv arXiv 2026

  24. [24]

    Gravity Spy: lessons learned and a path forward

    Zevin, Michael and others. Gravity Spy: lessons learned and a path forward. Eur. Phys. J. Plus. 2024. doi:10.1140/epjp/s13360-023-04795-4. arXiv:2308.15530

    work page doi:10.1140/epjp/s13360-023-04795-4 2024

  25. [25]

    and Udall, Rhiannon and Rink, Katie and Hourihane, Sophie and Miller, Simona J

    Lecoeuche, Yannick and McIver, Jess and Knee, Alan M. and Udall, Rhiannon and Rink, Katie and Hourihane, Sophie and Miller, Simona J. and Chatziioannou, Katerina and Massinger, TJ and Davis, Derek. Coalescing Compact Binary Parameter Estimation with Gravitational Waves in the Presence of non-Gaussian Transient Noise. arXiv:2604.07668. 2026. arXiv:2604.07668

    Pith/arXiv arXiv 2026

  26. [26]

    Inferring the spins of merging black holes in the presence of data-quality issues

    Udall, Rhiannon and Bini, Sophie and Chatziioannou, Katerina and Davis, Derek and Hourihane, Sophie and Lecoeuche, Yannick and McIver, Jess and Miller, Simona. Inferring the spins of merging black holes in the presence of data-quality issues. Phys. Rev. D. 2026. doi:10.1103/hyz2-3wxy. arXiv:2510.05029

    work page doi:10.1103/hyz2-3wxy 2026

  27. [27]

    and Davis, Derek and Dyer, Martin J

    Macas, Ronaldas and Pooley, Joshua and Nuttall, Laura K. and Davis, Derek and Dyer, Martin J. and Lecoeuche, Yannick and Lyman, Joseph D. and McIver, Jess and Rink, Katherine. Impact of noise transients on low latency gravitational-wave event localization. Phys. Rev. D. 2022. doi:10.1103/PhysRevD.105.103021. arXiv:2202.00344

    work page doi:10.1103/physrevd.105.103021 2022

  28. [28]

    and Farr, Will M

    Chatziioannou, Katerina and Haster, Carl-Johan and Littenberg, Tyson B. and Farr, Will M. and Ghonge, Sudarshan and Millhouse, Margaret and Clark, James A. and Cornish, Neil. Noise spectral estimation methods and their impact on gravitational wave measurement of compact binary mergers. Phys. Rev. D. 2019. doi:10.1103/PhysRevD.100.104004. arXiv:1907.06540

    work page doi:10.1103/physrevd.100.104004 2019

  29. [29]

    Transient glitch mitigation in Advanced LIGO data

    Merritt, Jonathan and Farr, Ben and Hur, Rachel and Edelman, Bruce and Doctor, Zoheyr. Transient glitch mitigation in Advanced LIGO data. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.104.102004. arXiv:2108.12044

    work page doi:10.1103/physrevd.104.102004 2021

  30. [30]

    and Mandic, Vuk and Seljak, Uro s and Stergioulas, Nikolaos

    Sasli, Argyro and Karamanis, Minas and Karnesis, Nikolaos and Coughlin, Michael W. and Mandic, Vuk and Seljak, Uro s and Stergioulas, Nikolaos. Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis. arXiv:2602.22074. 2026. arXiv:2602.22074

    arXiv 2026

  31. [31]

    Issues of mismodeling gravitational-wave data for parameter estimation

    Edy, Oliver and Lundgren, Andrew and Nuttall, Laura K. Issues of mismodeling gravitational-wave data for parameter estimation. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.103.124061. arXiv:2101.07743

    work page doi:10.1103/physrevd.103.124061 2021

  32. [32]

    Testing general relativity using golden black-hole binaries

    Ghosh, Abhirup and others. Testing general relativity using golden black-hole binaries. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.94.021101. arXiv:1602.02453

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.94.021101 2016

  33. [33]

    Abbott, B. P. and others. Tests of general relativity with GW150914. Phys. Rev. Lett. 2016. doi:10.1103/PhysRevLett.116.221101. arXiv:1602.03841

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevlett.116.221101 2016

  34. [34]

    and others

    Glanzer, J. and others. Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications. Class. Quant. Grav. 2023. doi:10.1088/1361-6382/acb633. arXiv:2208.12849

    work page doi:10.1088/1361-6382/acb633 2023

  35. [35]

    Joint inference of gravitational-wave signal and noise glitch

    Yin Cheung, Shun and Udall, Rhiannon and Davis, Derek and Lasky, Paul and Thrane, Eric. Joint inference of gravitational-wave signal and noise glitch. In preparation. 2026

    2026

  36. [36]

    and Haster, Carl-Johan and Varma, Vijay and Field, Scott E

    Islam, Tousif and Vajpeyi, Avi and Shaik, Feroz H. and Haster, Carl-Johan and Varma, Vijay and Field, Scott E. and Lange, Jacob and O'Shaughnessy, Richard and Smith, Rory. Analysis of GWTC-3 with fully precessing numerical relativity surrogate models. Phys. Rev. D. 2025. doi:10.1103/48ck-2fff. arXiv:2309.14473

    work page doi:10.1103/48ck-2fff 2025

  37. [37]

    Testing general relativity using gravitational wave signals from the inspiral, merger and ringdown of binary black holes

    Ghosh, Abhirup and Johnson-Mcdaniel, Nathan K. and Ghosh, Archisman and Mishra, Chandra Kant and Ajith, Parameswaran and Del Pozzo, Walter and Berry, Christopher P. L. and Nielsen, Alex B. and London, Lionel. Testing general relativity using gravitational wave signals from the inspiral, merger and ringdown of binary black holes. Class. Quant. Grav. 2018. ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1361-6382/aa972e 2018

  38. [38]

    Rapid and accurate parameter inference for coalescing, precessing compact binaries

    Lange, Jacob and O'Shaughnessy, Richard and Rizzo, Monica. Rapid and accurate parameter inference for coalescing, precessing compact binaries. arXiv:1805.10457. 2018. arXiv:1805.10457

    Pith/arXiv arXiv 2018

  39. [39]

    Laser Interferometer Space Antenna

    Amaro-Seoane, Pau and others. Laser Interferometer Space Antenna. arXiv:1702.00786. 2017. arXiv:1702.00786

    Pith/arXiv arXiv 2017

  40. [40]

    and Williams, Natalie and Zimmerman, Aaron

    Krishna, Kruthi and Vijaykumar, Aditya and Ganguly, Apratim and Talbot, Colm and Biscoveanu, Sylvia and George, Richard N. and Williams, Natalie and Zimmerman, Aaron. Accelerated parameter estimation in Bilby with relative binning. arXiv:2312.06009. 2023. arXiv:2312.06009

    arXiv 2023

  41. [41]

    Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions

    Morisaki, Soichiro. Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.104.044062. arXiv:2104.07813

    work page doi:10.1103/physrevd.104.044062 2021

  42. [42]

    Heterodyned likelihood for rapid gravitational wave parameter inference

    Cornish, Neil J. Heterodyned likelihood for rapid gravitational wave parameter inference. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.104.104054. arXiv:2109.02728

    work page doi:10.1103/physrevd.104.104054 2021

  43. [43]

    Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature

    Morisaki, Soichiro and Raymond, Vivien. Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature. Phys. Rev. D. 2020. doi:10.1103/PhysRevD.102.104020. arXiv:2007.09108

    work page doi:10.1103/physrevd.102.104020 2020

  44. [44]

    Relative Binning and Fast Likelihood Evaluation for Gravitational Wave Parameter Estimation

    Zackay, Barak and Dai, Liang and Venumadhav, Tejaswi. Relative Binning and Fast Likelihood Evaluation for Gravitational Wave Parameter Estimation. arXiv:1806.08792. 2018. arXiv:1806.08792

    Pith/arXiv arXiv 2018

  45. [45]

    Accelerating gravitational wave parameter estimation with multi-band template interpolation

    Vinciguerra, Serena and Veitch, John and Mandel, Ilya. Accelerating gravitational wave parameter estimation with multi-band template interpolation. Class. Quant. Grav. 2017. doi:10.1088/1361-6382/aa6d44. arXiv:1703.02062

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1361-6382/aa6d44 2017

  46. [46]

    Accelerated gravitational-wave parameter estimation with reduced order modeling

    Canizares, Priscilla and Field, Scott E. and Gair, Jonathan and Raymond, Vivien and Smith, Rory and Tiglio, Manuel. Accelerated gravitational-wave parameter estimation with reduced order modeling. Phys. Rev. Lett. 2015. doi:10.1103/PhysRevLett.114.071104. arXiv:1404.6284

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevlett.114.071104 2015

  47. [47]

    Fast Fisher Matrices and Lazy Likelihoods

    Cornish, Neil J. Fast Fisher Matrices and Lazy Likelihoods. arXiv:1007.4820. 2010. arXiv:1007.4820

    Pith/arXiv arXiv 2010

  48. [48]

    BILBY in space: Bayesian inference for transient gravitational-wave signals observed with LISA

    Hoy, Charlie and Nuttall, Laura K. BILBY in space: Bayesian inference for transient gravitational-wave signals observed with LISA. Mon. Not. Roy. Astron. Soc. 2024. doi:10.1093/mnras/stae646. arXiv:2312.13039

    work page doi:10.1093/mnras/stae646 2024

  49. [49]

    Abbott, B. P. and others. Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914. Class. Quant. Grav. 2016. doi:10.1088/0264-9381/33/13/134001. arXiv:1602.03844

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/0264-9381/33/13/134001 2016

  50. [50]

    Modeling compact binary signals and instrumental glitches in gravitational wave data

    Chatziioannou, Katerina and Cornish, Neil and Wijngaarden, Marcella and Littenberg, Tyson B. Modeling compact binary signals and instrumental glitches in gravitational wave data. Phys. Rev. D. 2021. doi:10.1103/PhysRevD.103.044013. arXiv:2101.01200

    work page doi:10.1103/physrevd.103.044013 2021

  51. [51]

    Ghonge, Sudarshan and Brandt, Joshua and Sullivan, J. M. and Millhouse, Margaret and Chatziioannou, Katerina and Clark, James A. and Littenberg, Tyson and Cornish, Neil and Hourihane, Sophie and Cadonati, Laura. Assessing and mitigating the impact of glitches on gravitational-wave parameter estimation: A model agnostic approach. Phys. Rev. D. 2024. doi:10...

    work page doi:10.1103/physrevd.110.122002 2024

  52. [52]

    Thorne, K. S. Multipole Expansions of Gravitational Radiation. Rev. Mod. Phys. 1980. doi:10.1103/RevModPhys.52.299

    work page doi:10.1103/revmodphys.52.299 1980

  53. [53]

    Goldberg, J. N. and MacFarlane, A. J. and Newman, E. T. and Rohrlich, F. and Sudarshan, E. C. G. Spin- s spherical harmonics and. J. Math. Phys. 1967. doi:10.1063/1.1705135

    work page doi:10.1063/1.1705135 1967

  54. [54]

    and Cutler, Curt and Sussman, Gerald J

    Apostolatos, Theocharis A. and Cutler, Curt and Sussman, Gerald J. and Thorne, Kip S. Spin induced orbital precession and its modulation of the gravitational wave forms from merging binaries. Phys. Rev. D. 1994. doi:10.1103/PhysRevD.49.6274

    work page doi:10.1103/physrevd.49.6274 1994

  55. [55]

    Improving the Sensitivity of Advanced LIGO Using Noise Subtraction

    Davis, D. and Massinger, T. J. and Lundgren, A. P. and Driggers, J. C. and Urban, A. L. and Nuttall, L. K. Improving the Sensitivity of Advanced LIGO Using Noise Subtraction. Class. Quant. Grav. 2019. doi:10.1088/1361-6382/ab01c5. arXiv:1809.05348

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1361-6382/ab01c5 2019

  56. [56]

    Automatic cross talk removal from multichannel data

    Allen, Bruce and Hua, Wen-sheng and Ottewill, Adrian C. Automatic cross talk removal from multichannel data. arXiv:gr-qc/9909083. 1999. arXiv:gr-qc/9909083

    Pith/arXiv arXiv 1999

  57. [57]

    Mitigation of the instrumental noise transient in gravitational-wave data surrounding GW170817

    Pankow, Chris and others. Mitigation of the instrumental noise transient in gravitational-wave data surrounding GW170817. Phys. Rev. D. 2018. doi:10.1103/PhysRevD.98.084016. arXiv:1808.03619

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.98.084016 2018

  58. [58]

    and Littenberg, T

    Davis, D. and Littenberg, T. B. and Romero-Shaw, I. M. and Millhouse, M. and McIver, J. and Di Renzo, F. and Ashton, G. Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run. Class. Quant. Grav. 2022. doi:10.1088/1361-6382/aca238. arXiv:2207.03429

    work page doi:10.1088/1361-6382/aca238 2022

  59. [59]

    Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

    Macas, Ronaldas and Lundgren, Andrew and Ashton, Gregory. Revisiting the evidence for precession in GW200129 with machine learning noise mitigation. Phys. Rev. D. 2024. doi:10.1103/PhysRevD.109.062006. arXiv:2311.09921

    work page doi:10.1103/physrevd.109.062006 2024

  60. [60]

    General-relativistic precession in a black-hole binary

    Hannam, Mark and others. General-relativistic precession in a black-hole binary. Nature. 2022. doi:10.1038/s41586-022-05212-z. arXiv:2112.11300

    work page doi:10.1038/s41586-022-05212-z 2022

  61. [61]

    Curious case of GW200129: Interplay between spin-precession inference and data-quality issues

    Payne, Ethan and Hourihane, Sophie and Golomb, Jacob and Udall, Rhiannon and Udall, Richard and Davis, Derek and Chatziioannou, Katerina. Curious case of GW200129: Interplay between spin-precession inference and data-quality issues. Phys. Rev. D. 2022. doi:10.1103/PhysRevD.106.104017. arXiv:2206.11932

    work page doi:10.1103/physrevd.106.104017 2022

  62. [62]

    dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences

    Speagle, Joshua S. dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Mon. Not. Roy. Astron. Soc. 2020. doi:10.1093/mnras/staa278. arXiv:1904.02180

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1093/mnras/staa278 2020

  63. [63]

    Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation, and sky localization

    Roulet, Javier and Mushkin, Jonathan and Wadekar, Digvijay and Venumadhav, Tejaswi and Zackay, Barak and Zaldarriaga, Matias. Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation, and sky localization. Phys. Rev. D. 2024. doi:10.1103/PhysRevD.110.044010. arXiv:2404.02435

    work page doi:10.1103/physrevd.110.044010 2024

  64. [64]

    Sampler-free gravitational wave inference using matrix multiplication

    Mushkin, Jonathan and Roulet, Javier and Zackay, Barak and Venumadhav, Tejaswi and Ivashtenko, Oryna and Wadekar, Digvijay and Mehta, Ajit Kumar and Zaldarriaga, Matias. Sampler-free gravitational wave inference using matrix multiplication. Phys. Rev. D. 2025. doi:10.1103/vqj2-7qpz. arXiv:2507.16022

    work page doi:10.1103/vqj2-7qpz 2025

  65. [65]

    and Chatziioannou, Katerina

    Roulet, Javier and Crisostomi, Marco and Thomas, Lucy M. and Chatziioannou, Katerina. labrador: A domain-optimized machine-learning tool for gravitational wave inference. arXiv:2604.08897. 2026. arXiv:2604.08897

    Pith/arXiv arXiv 2026

  66. [66]

    Biwer, C. M. and Capano, Collin D. and De, Soumi and Cabero, Miriam and Brown, Duncan A. and Nitz, Alexander H. and Raymond, V. PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals. Publ. Astron. Soc. Pac. 2019. doi:10.1088/1538-3873/aaef0b. arXiv:1807.10312

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1538-3873/aaef0b 2019

  67. [67]

    Romero-Shaw, I. M. and others. Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO Virgo gravitational-wave transient catalogue. Mon. Not. Roy. Astron. Soc. 2020. doi:10.1093/mnras/staa2850. arXiv:2006.00714

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1093/mnras/staa2850 2020

  68. [68]

    Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy

    Ashton, Gregory and others. BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy. Astrophys. J. Suppl. 2019. doi:10.3847/1538-4365/ab06fc. arXiv:1811.02042

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.3847/1538-4365/ab06fc 2019

  69. [69]

    Simulation-based inference for gravitational-waves from intermediate-mass binary black holes in real noise

    Raymond, Vivien and Al-Shammari, Sama and G. Simulation-based inference for gravitational-waves from intermediate-mass binary black holes in real noise. Mon. Not. Roy. Astron. Soc. 2025. doi:10.1093/mnras/staf1282. arXiv:2406.03935

    work page doi:10.1093/mnras/staf1282 2025

  70. [70]

    and Gair, Jonathan and Gupte, Nihar and P

    Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Gupte, Nihar and P. Real-time inference for binary neutron star mergers using machine learning. Nature. 2025. doi:10.1038/s41586-025-08593-z. arXiv:2407.09602

    work page doi:10.1038/s41586-025-08593-z 2025

  71. [71]

    and Gair, Jonathan and Macke, Jakob H

    Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Macke, Jakob H. and Buonanno, Alessandra and Sch. Real-Time Gravitational Wave Science with Neural Posterior Estimation. Phys. Rev. Lett. 2021. doi:10.1103/PhysRevLett.127.241103. arXiv:2106.12594

    work page doi:10.1103/physrevlett.127.241103 2021

  72. [72]

    and Simpson, Christine and Gair, Jonathan

    Green, Stephen R. and Simpson, Christine and Gair, Jonathan. Gravitational-wave parameter estimation with autoregressive neural network flows. Phys. Rev. D. 2020. doi:10.1103/PhysRevD.102.104057. arXiv:2002.07656

    work page doi:10.1103/physrevd.102.104057 2020

  73. [73]

    and Gair, Jonathan

    Green, Stephen R. and Gair, Jonathan. Complete parameter inference for GW150914 using deep learning. Mach. Learn. Sci. Tech. 2021. doi:10.1088/2632-2153/abfaed. arXiv:2008.03312

    work page doi:10.1088/2632-2153/abfaed 2021

  74. [74]

    Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

    Gabbard, Hunter and Messenger, Chris and Heng, Ik Siong and Tonolini, Francesco and Murray-Smith, Roderick. Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. Nature Phys. 2022. doi:10.1038/s41567-021-01425-7. arXiv:1909.06296

    work page doi:10.1038/s41567-021-01425-7 2022

  75. [75]

    Simple parameter estimation using observable features of gravitational-wave signals

    Fairhurst, Stephen and Hoy, Charlie and Green, Rhys and Mills, Cameron and Usman, Samantha A. Simple parameter estimation using observable features of gravitational-wave signals. Phys. Rev. D. 2023. doi:10.1103/PhysRevD.108.082006. arXiv:2304.03731

    work page doi:10.1103/physrevd.108.082006 2023

  76. [76]

    A novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences

    Pankow, C. and Brady, P. and Ochsner, E. and O'Shaughnessy, R. Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.92.023002. arXiv:1502.04370

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.92.023002 2015

  77. [77]

    Fast non-Markovian sampler for estimating gravitational-wave posteriors

    Tiwari, Vaibhav and Hoy, Charlie and Fairhurst, Stephen and MacLeod, Duncan. Fast non-Markovian sampler for estimating gravitational-wave posteriors. Phys. Rev. D. 2023. doi:10.1103/PhysRevD.108.023001. arXiv:2303.01463

    work page doi:10.1103/physrevd.108.023001 2023

  78. [78]

    Nested Sampling

    Skilling, John. Nested Sampling. AIP Conf. Proc. 2004. doi:10.1063/1.1835238

    work page doi:10.1063/1.1835238 2004

  79. [79]

    Nested sampling for general Bayesian computation

    Skilling, John. Nested sampling for general Bayesian computation. Bayesian Analysis. 2006. doi:10.1214/06-BA127

    work page doi:10.1214/06-ba127 2006

  80. [80]

    Detection, Measurement and Gravitational Radiation

    Finn, Lee S. Detection, measurement and gravitational radiation. Phys. Rev. D. 1992. doi:10.1103/PhysRevD.46.5236. arXiv:gr-qc/9209010

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.46.5236 1992

Showing first 80 references.