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REVIEW 3 major objections 6 minor 1 cited by

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

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A normalising flow trained on real Blip glitches, embedded in a standard Bayesian inference code, allows gravitational-wave signal and glitch parameters to be fitted jointly; the paper reports that this removes the glitch and reduces…

desk verdict A solid, honest proof-of-concept for a data-informed glitch prior in Bilby; the core idea is new and works, but the authors should verify the glitch model is not absorbing signal power in clean data and should give readers a baseline comparison. read the letter →

arxiv 2505.00657 v2 pith:HTAN7J7B submitted 2025-05-01 gr-qc astro-ph.HEastro-ph.IM

Ann-Kristin Malz , John Veitch This is my paper · ORCID
classification gr-qcastro-ph.HEastro-ph.IM
keywords gravitationalwavesglitchmitigationnormalisingflowsBayesianinferencesingularvaluedecompositionLIGObinaryblackholeparameterestimationtransientnoise
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Gravitational-wave detectors are frequently hit by short noise transients, glitches, that can masquerade as or distort astrophysical signals. This paper tries to establish that a glitch model whose prior is learned from real detector glitches—rather than from a generic wavelet basis—can be inserted into the standard Bayesian inference pipeline, so that signal and glitch parameters are estimated jointly and the glitch is removed from the data. The authors demonstrate this on real LIGO data from the first and third observing runs, injecting binary-black-hole signals on top of actual Blip glitches. They report that the joint signal-plus-glitch model is preferred in most test cases, that glitch-free data rarely trigger the model, and that recovered signal parameters are substantially less biased after the glitch is removed. If the result holds, it offers a practical, data-informed alternative to pre-subtraction or agnostic wavelet fitting for glitch mitigation in gravitational-wave astronomy.

What carries the argument

The load-bearing mechanism is the pairing of a low-dimensional SVD subspace of observed glitches with a normalising-flow prior over the subspace coordinates. A normalising flow is a machine-learning density estimator built from invertible transformations, which can both evaluate the probability of a set of glitch amplitudes and generate new glitch realisations by sampling a latent Gaussian. The training pipeline bandpasses 20–400 Hz, whitens, cuts to 1/8 s, applies a Hann window, and keeps enough singular vectors to retain 97% of the power, yielding 12 amplitude parameters per glitch. The flow learns the distribution of these 12 parameters; at inference time the amplitudes are drawn from the flow, multiplied by the fixed basis, shifted in time under a 10-ms Gaussian prior, and scaled by an amplitude factor, and the subtracted glitch leaves a residual whose Gaussian likelihood scores the signal parameters. This construction is what lets the analysis move from an agnostic wavelet description of transients to one informed by the actual population of glitches.

What would settle it

Take a sample of Blip glitches from a later observing run not used in training, inject a signal with known parameters into each, and measure the standard accuracy of the recovered parameters with and without the joint glitch model: if the model does not bring the bias down to about 1, or if the signal-plus-glitch model is not preferred in most cases, the central claim fails. The paper's Tomte experiment already provides a partial version of this test, with a 22% false-alarm rate and joint-model preference in only 15% of cases when the prior does not match the glitch class.

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Extended reading notes

Core claim

The central claim is that a parameterised glitch model with a data-learned prior can be jointly inferred with a gravitational-wave signal, and that doing so removes the glitch and reduces bias in the recovered source parameters. The paper states this directly: modelling Blip glitches using normalising flows can successfully remove glitches from the data, thus reducing bias in the signal parameter estimation. The demonstration uses real O1 and O3 glitches: a flow trained on O1 Blip glitches is applied to unseen glitches, separates glitch from Gaussian background in log Bayes factor, and when a known binary signal is injected over a glitch, the signal-plus-glitch model is preferred over the signal-only model for the majority of test cases. Quantitatively, the mean standard accuracy of recovered parameters moves toward about 1 when the glitch model is included, whereas the glitch-contaminated analysis shows biases several times larger for the most affected parameters.

Load-bearing premise

The load-bearing assumption is that the Blip glitches in the data being analysed look like the O1 Blip glitches used for training—same morphology, same 20–400 Hz band, same short duration—so the learned SVD subspace and flow prior cover the target glitch, and the paper's Tomte results show what happens when they do not.

Editorial extensions

If this is right

  • Existing compact-binary analyses could adopt the method without changing their inference software: the glitch prior plugs into the same likelihood and sampler used for standard parameter estimation.
  • For Blip-class contamination, glitch-removed posteriors should have standard accuracy near 1 across mass, distance, and sky-location parameters, rather than the multi-sigma biases seen when the glitch is ignored.
  • Training a new model is cheap, so the procedure can be re-run for each observing run or detector configuration to keep the prior matched to current glitch populations.
  • The technique is not restricted to Blip glitches; a quick retraining on Koi Fish and Power Line glitches also yields a model that removes those glitches.
  • Using a glitch model trained on a morphologically similar but different class still reduces parameter bias, but model selection becomes unreliable, so the paper recommends training one model per glitch class.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the SVD-plus-flow representation generalises as cleanly as the Blip results suggest, the same glitch-prior plugin could be used as a transient model for unmodelled astrophysical bursts, where no waveform template is available for joint inference.
  • The strong separation in log Bayes factors between glitch and no-glitch data indicates the glitch evidence itself could serve as a data-quality flag, not just a parameter-estimation fix, with thresholds set from the background distribution.
  • A mixture-of-flows prior spanning several glitch classes would be a natural extension; it would let the sampler choose which class is present rather than committing to one trained morphology, and would likely address the Tomte failure mode.
  • The per-run retraining requirement suggests a monitoring use: periodic retraining on fresh glitch classifications would keep the prior aligned as detector noise evolves, turning the method into a continuous glitch-calibration tool.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper develops a data-informed parametric glitch model for LIGO data. Blip glitches from the Gravity Spy O1 training set are decomposed with SVD into a 12-dimensional basis, and a normalizing flow is trained on the coefficients to form a prior. This prior is implemented in Bilby, together with an amplitude scale and a time-shift parameter, so that signal and glitch parameters are inferred jointly. The authors validate the model on real O1 and O3 glitches with injected signals, reporting Bayes-factor separation between glitch and noise data, model-selection tests for signal-only, glitch-only, and glitch+signal models, and standard-accuracy bias metrics before and after glitch removal. They find that the joint model is preferred for most Blip test cases and that the recovered signal parameters are less biased after glitch removal.

Significance. If the clean-data signal-absorption issue is resolved, this is a valuable contribution. The method sits between agnostic wavelet models such as BayesWave and fully physical glitch models, and it is integrated into the standard Bilby workflow, which lowers barriers to adoption. The public code and trained model, the use of real glitches from three observing runs, and the explicit transfer test to Tomte glitches are strengths, and the validation is more extensive than in many similar proof-of-concept papers. The paper is clearly written and the conclusions are appropriately cautious about the need to retrain for other glitch classes. However, the absence of a signal-parameter check on glitch-free data leaves a gap in the central claim that glitch removal, rather than partial signal absorption, is responsible for the reported bias reduction.

major comments (3)
  1. [Section IV B, Fig. 7] The signal-only validation reports only log Bayes factors, not the signal posterior recovered by the glitch+signal model on glitch-free data. Because the glitch model contains 12 SVD coefficients plus a scale parameter A with prior 1e-3 to 1e3 and a 10-ms time shift, it can partially absorb a compact-binary merger waveform, which is concentrated in the same 1/8-s window. If this occurs, the bias reduction in Section IV D could be partly caused by removing signal power rather than glitch power. Please add a glitch-free injection test that compares the posterior moments, or the standard accuracy, of the reported parameters between the signal-only and glitch+signal analyses, and report both the model evidence and the recovered posteriors.
  2. [Sections III B and IV] The results depend on several ad hoc preprocessing choices: the 20-400 Hz bandpass, the 97% SVD power cutoff, the 1/8-s window, the 10-ms time-shift prior, and the A prior range, but no sensitivity analysis is given. Since the paper's central claim is that the method effectively removes glitches and reduces bias, the reader cannot tell whether the reported Bayes-factor separation and standard-accuracy improvement are robust to reasonable variations of these choices or are tuned to the specific settings. Please add a sensitivity test varying at least the SVD cutoff, the window length, and the bandpass edges, and report the effect on the foreground/background separation and on the standard accuracy for a subset of O3 glitches.
  3. [Section IV E and Section V] The Tomte analysis concludes that even an incorrect glitch model improves signal analysis, but the same section reports that the glitch+signal model is preferred in only 15% of the 20 injections and that the foreground false-alarm rate is 22%. These two observations need to be reconciled explicitly, for example by showing that the standard-accuracy improvement is not driven by a few events and by reporting per-event standard accuracy or a model-averaged posterior. Without this, the claim that an incorrect model still reduces bias is not fully supported, and the conclusion is more fragile than the Blip-only claims.
minor comments (6)
  1. [Equation (4)] The quantity xmaxL is described as the 'maximum likelihood posterior value'; this wording is ambiguous. Please state whether it is the maximum-likelihood sample, the maximum-a-posteriori value, or the maximum of the marginal posterior.
  2. [Section IV A 1] The O1 foreground results in Fig. 5 use glitches that are in the training set, while the O3 results use held-out glitches. Please state this explicitly in the text, or separate the in-sample and out-of-sample O1 results, so the reader can gauge the potential optimism from training on the same glitches.
  3. [Section IV C, Fig. 10] The y-axis is described as the 'ratio of log Bayes factors', but the text and the threshold at one indicate that the plotted quantity is the log Bayes factor between the glitch+signal and signal-only models. Please clarify the exact quantity plotted.
  4. [Section IV D, Figs. 12, 13, 16] The mean standard accuracy is reported without uncertainties or per-event scatter. Adding error bars or showing the distribution would make the comparisons more interpretable given the small sample sizes of 20-25 glitches.
  5. [Section V] A direct comparison, or at least an explicit discussion, with BayesWave and gwsubtract on the same test glitches would help the reader judge the practical added value of the new model, since these are the standard methods in LVK analyses.
  6. [Section III D] Please specify whether the 10-ms Gaussian time-shift prior is centered on the Omicron trigger time and whether that trigger time is the one reported in the Gravity Spy catalogue.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the normalising-flow glitch prior is an input, and the headline Bayes factors and bias metrics are evaluated on injected signals and held-out glitches.

full rationale

The paper's derivation chain is not circular. The normalising flow is trained only on glitch amplitudes T from an SVD decomposition of O1 Gravity Spy Blip glitches (Section III A-C); this trained prior is then inserted into Bilby's likelihood as an input model. The headline results - glitch-versus-noise Bayes factors, signal-plus-glitch model selection, and standard-accuracy bias metrics - are all computed on data that are not used to fit the NF: injected signals have known true parameters, glitch tests include unseen O2/O3a/O3b glitches, and the bias reduction is measured by comparing posteriors with and without the glitch model. No equation in the paper defines the target result in terms of the fitted prior; the Bayes factor is an evidence ratio (Eq. 3) over the full likelihood, and the standard accuracy (Eq. 4) is evaluated against injected values. The O1 in-sample component is disclosed ('This was done both for glitches present in the training data (O1) and for unseen glitches from the O2, O3a, and O3b runs') and is not the sole evidence, since O3 results are reported throughout. The Tomte experiments (Section IV E) explicitly show that the model can fail, with a 22% false alarm rate and the joint model preferred in only 15% of signal-injection cases, confirming that the tests are not tautological. The paper also states in Section V that 'the model is only knowledgeable about these types of glitches as they appeared during O1,' an honest limitation rather than a circular move. Self-citations (nessai sampler [60], glasflow [59], and Veitch and Vecchio [62]) are software/tool citations or a peripheral model-selection remark; none carry the central argument. The skeptic concern that the glitch model could absorb signal power is a robustness limitation of the Section IV B test, not a circularity: the glitch model is flexible, but the paper's own model-selection results on glitch-free data show the signal-only model is preferred, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The method rests on a set of user-chosen hyperparameters (SVD cutoff, bandpass, window, prior widths) and domain assumptions about noise, linearity, and representativeness of the training set. The neural network weights are fitted to data, but the central validation is out-of-sample on O3 glitches, so the burden is moderate. No new physical entities are introduced.

free parameters (7)
  • Number of SVD components = 12 (97% power cutoff)
    Chosen to retain 97% of glitch power; not independently determined; increasing it improves fit but raises cost.
  • Bandpass filter = 20-400 Hz
    Set to cover 92% of training glitch peak frequencies; excludes higher-frequency noise lines; ad hoc.
  • Time window = 1/8 s
    Shortened from 1 s because Blip glitches are ~10 ms; choice affects model degrees of freedom.
  • Time shift prior width = 10 ms (Gaussian std)
    Accounts for trigger time uncertainty; chosen by hand.
  • Amplitude scaling prior = 1/A on [1e-3, 1e3]
    Allows amplitude adjustment while preserving shape; broad range chosen by hand.
  • Whitening ASD = O1 representative ASD per detector
    Used for training data whitening; un-whitened at test time with run-specific ASD; mismatch handled with amplitude scaling A.
  • NF hyperparameters = not stated in text
    CouplingNSF architecture details (layers, hidden units) are not given; they live in the code, affecting prior fidelity.
assumptions (6)
  • domain assumption Gaussian noise likelihood after glitch subtraction
    The likelihood assumes residual data is Gaussian noise; if glitch subtraction is imperfect or noise non-stationary, this is violated.
  • domain assumption Gravity Spy classifications are correct
    Training labels and test glitch times come from citizen science and ML classifications; mislabeled glitches would bias the prior.
  • domain assumption Blip glitches in O2/O3/O4 are similar to O1 training set
    The model is trained only on O1 Blips; Section V acknowledges detector changes may create new glitch types.
  • standard math Signal and glitch add linearly in strain data
    The data model d = s + n + g assumes linear superposition, standard for GW transients.
  • ad hoc to paper SVD basis captures all relevant glitch features at 97% power cutoff
    Truncation discards the weakest 3% of power; glitches with morphology orthogonal to retained basis are not modeled.
  • standard math Nested sampling (nessai) converges for the 12-14 parameter joint model
    Valid nested sampling requires convergence; run times are given, but convergence diagnostics are not shown.

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Cite this review

Pith. "Pith review of Joint inference for gravitational wave signals and glitches using a data-informed glitch model." pith.science (2026). https://pith.science/paper/HTAN7J7B

@misc{pith2026250500657,
  author       = {Pith},
  title        = {Pith review of: Joint inference for gravitational wave signals and glitches using a data-informed glitch model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HTAN7J7B}},
  note         = {Machine review of arXiv:2505.00657}
}
read the original abstract

Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inference analysis with Bilby, we estimate glitch and signal parameters simultaneously. We demonstrate the performance of our method through bias reduction, glitch identification and Bayesian model selection on real glitches. Our results show that this approach effectively removes glitches from the data, significantly improving source parameter estimation and reducing bias.

Figures

Figures reproduced from arXiv: 2505.00657 by the authors.

Figure 1
Figure 1. FIG. 1. The 12 components of the [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Illustration of glitch reconstruction. Latent param [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. (Upper panel) Plot of an O3 Blip glitch (GPS time [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Q-scan plots of the O3 Blip glitch shown in Fig. 3. The plot on the left shows the data pre-glitch removal, while in the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Background: Histogram of log Bayes factors obtained from applying the glitch model to glitch-free O1 interferometer [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Background: Histogram of Bayes factors obtained from applying the glitch model to glitch-free O3 interferometer data [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Plot of data containing a glitch during an injected [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. The ternary plot shows the log Bayes factors (as [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 14
Figure 14. Figure 14: Similarly to the case with Blip glitches in the [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]
Figure 11
Figure 11. Figure 11: FIG. 11. Corner plots of signal posteriors, inferred before and after the glitch was removed from the data, respectively. For [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Mean standard accuracy over 25 O1 test glitches, [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 14
Figure 14. Figure 14: FIG. 14. Foreground histogram of log Bayes factors from [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]
Figure 15
Figure 15. Figure 15: FIG. 15. The plot shows the log Bayes factors when applying [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]

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Forward citations

Cited by 1 Pith paper

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