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arxiv: 2604.21859 · v2 · pith:TWFNHI6Vnew · submitted 2026-04-23 · 🌌 astro-ph.HE · astro-ph.IM· gr-qc

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data

Sumit Kumar , Max Melching , Frank Ohme , Harsh Narola , Tom Dooney , Chris Van Den Broeck This is my paper

Pith reviewed 2026-05-19 16:57 UTC · model grok-4.3

classification 🌌 astro-ph.HE astro-ph.IMgr-qc
keywords gravitational wavesparameter estimationsystematic errorsbinary black holesLIGO-Virgowaveform uncertaintiesspin precessiondata artifacts
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The pith

Parametric uncertainty models for waveforms reduce systematic errors in LIGO-Virgo binary black hole parameter estimates and align results across models and data versions.

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

The paper reanalyzes selected events from the first three LIGO-Virgo observing runs that prior studies flagged for possible systematic biases. It applies parametric models that add free parameters for phase and amplitude deviations in the gravitational waveform, using deliberately wide priors so the model can absorb errors from glitches, deglitching, or waveform approximations. For the event GW200129_065458 this produces consistent non-zero precession values near 0.58 across three different waveform families. For GW191109_010717 the anti-aligned spin signature survives but now agrees between raw and deglitched data files. A reader would care because more robust parameter values improve population studies and tests of general relativity with the growing catalog of detections.

Core claim

The central claim is that equipping gravitational-wave parameter estimation with parametric phase and amplitude uncertainty models, together with sufficiently broad priors on those uncertainty parameters, absorbs the dominant systematic errors present in selected O1-O3 events. This absorption renders astrophysical inferences, such as spin precession and spin alignment, consistent across waveform models (IMRPhenomXPHM, IMRPhenomXO4a, NRSur7dq4) and across raw versus deglitched strain data, even when the original analyses disagreed because of glitches near the signal or model differences.

What carries the argument

Parametric models that introduce free parameters for deviations in the phase and amplitude of the gravitational waveform, used with broad priors to marginalize over systematic mismatches.

If this is right

  • Inconsistent precession measurements for GW200129_065458 become consistent non-zero values of approximately 0.58 across three waveform models.
  • Anti-aligned spin inferences for GW191109_010717 remain but agree between raw and deglitched frame files and across the same three waveform models.
  • Systematic errors from glitches occurring near a signal or from the deglitching process are reduced without manual removal of data segments.
  • Parameter estimation results become less sensitive to the choice of waveform approximant when the uncertainty parameters are included.

Where Pith is reading between the lines

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

  • Population analyses that combine many events could adopt these uncertainty models as a standard step to reduce scatter in inferred spin distributions.
  • Events that still show large uncertainty parameters after the fit could be flagged for deeper follow-up with more expensive numerical-relativity waveforms.
  • The approach may generalize to other classes of signals, such as neutron-star mergers, where waveform modeling uncertainties are also large.

Load-bearing premise

The parametric uncertainty models can absorb the dominant systematic errors without introducing new biases into the astrophysical parameters.

What would settle it

Reanalysis of a simulated binary black hole signal with a known injected glitch and known true parameters; if the recovered astrophysical parameters remain biased away from the injected values even after the uncertainty parameters are marginalized, the method fails to mitigate the systematics.

Figures

Figures reproduced from arXiv: 2604.21859 by Chris Van Den Broeck, Frank Ohme, Harsh Narola, Max Melching, Sumit Kumar, Tom Dooney.

Figure 1
Figure 1. Figure 1: FIG. 1. This figure shows the distribution of compact binary mergers from the GWTC catalogs [ [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Here we show the cumulative distribution functions (CDF) of waveform mismatches for the GW events considered in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. The hexbin plots showing the mean logarithmic waveform mismatch (between waveform pair [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. This figure summarizes the PE result of different events for three key parameters: source frame chirp mass ( [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. The recovery of relative phase uncertainties, [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. This figure illustrates the differences in 1D marginalized posterior samples for event GW191109 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. The figure shows the 1D marginalized posterior distribution for the single detector runs for the event GW191109. We [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. The figure shows the 1D marginalized posterior samples of the waveform uncertainty parameter [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. This figure illustrates the differences in 1D marginalized posterior samples for event GW200129 [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Here we show the 1D marginalized posterior samples for parameters: [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. The figure illustrates the recovery of the relative phase uncertainty parameter [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. This figure summarizes the PE result for different events with the following parameters: mass ratio ( [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. This figure highlights the differences in the PE runs for the old and new calibration schemes for the event GW191109. [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
read the original abstract

Systematic errors in the parameter estimation (PE) of gravitational wave (GW) mergers can arise from various sources, including waveform systematics, noise mischaracterization, data analysis artifacts, and other unknown factors. In this study, we analyze selected events from the first three observing runs of the LIGO-Virgo-KAGRA (LVK) collaboration. We choose events that have been flagged in various studies as potentially affected by systematic errors. Here, we reanalyze these events using a couple of parametric models developed in previous work that incorporate uncertainties in both the phase and amplitude of the GW waveform. In this data-driven approach, we apply sufficiently broad priors on the uncertainty parameters to account for potential systematic errors. Our findings show that the proposed method effectively reduces systematic errors, even those arising from data artifacts, such as glitches occurring near a signal and the deglitching process in GW frame files. Similarly, inconsistent results from different waveform models become much more consistent in our framework. One noteworthy event we examine is GW191109\_010717, which is particularly interesting due to its anti-aligned spin properties. We report that, within our framework, the event still exhibits anti-aligned spin characteristics, but the inference results become consistent across raw and deglitched frame files, as well as across the waveform models used for this event (IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4). A similar trend is observed for the event GW200129\_065458, which previously yielded a high, but inconsistent precession parameter among different waveform models. In contrast, we observe a non-zero and consistent value of $\chi_{p}=0.60^{+0.31}_{-0.33}, 0.58^{+0.30}_{-0.29}$ and $0.56^{+0.31}_{-0.28}$ for the IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4 waveform models, respectively.

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 manuscript proposes a data-driven method to mitigate systematic errors in parameter estimation of binary black hole mergers from LIGO-Virgo O1-O3 data. It reanalyzes selected events flagged for potential systematics (including GW191109_010717 and GW200129_065458) by augmenting standard waveform models with parametric phase and amplitude uncertainty models equipped with broad priors. The central claim is that this approach reduces discrepancies arising from waveform model differences and data artifacts such as glitches and deglitching, yielding consistent astrophysical inferences (e.g., non-zero precession parameter χ_p ≈ 0.58 across IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4 for GW200129_065458, and preserved anti-aligned spin for GW191109_010717 across raw and deglitched frames).

Significance. If validated, the approach could provide a practical route to more robust spin and precession measurements for events affected by known data-quality issues, complementing existing waveform-model comparisons. The work applies previously developed parametric uncertainty models to real LVK events and reports concrete overlapping credible intervals, which is a positive step toward falsifiable improvements in systematic-error control.

major comments (2)
  1. [Results section (GW200129_065458 and GW191109_010717 analyses)] The central claim that broad priors on the phase/amplitude uncertainty parameters absorb dominant systematics (including glitches) without biasing astrophysical parameters rests on real-event analyses without ground truth. No injection-recovery tests with simulated signals containing known parameters plus injected glitches or deglitching artifacts are reported; such tests are required to distinguish genuine error mitigation from posterior broadening or new degeneracies introduced by the additional free parameters.
  2. [Discussion of χ_p results and prior choices] The reported consistency in χ_p for GW200129_065458 (0.60^{+0.31}_{-0.33}, 0.58^{+0.30}_{-0.29}, 0.56^{+0.31}_{-0.28} across the three waveform models) could arise in part from the added model flexibility rather than independent error reduction. The manuscript should quantify the information loss or degeneracy introduced by the uncertainty parameters (e.g., via prior-posterior overlap or effective degrees of freedom) and compare against a baseline without uncertainty parameters.
minor comments (1)
  1. [Abstract and results] The abstract and results text use inconsistent notation for event names (e.g., GW191109_010717 with escaped underscore); standardize to standard LVK naming convention throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments on our manuscript. We appreciate the positive assessment of the potential utility of our data-driven approach for mitigating systematics in gravitational-wave parameter estimation. We address each major comment below, agreeing where revisions are warranted and outlining specific changes to strengthen the validation of our results.

read point-by-point responses

  1. Referee: [Results section (GW200129_065458 and GW191109_010717 analyses)] The central claim that broad priors on the phase/amplitude uncertainty parameters absorb dominant systematics (including glitches) without biasing astrophysical parameters rests on real-event analyses without ground truth. No injection-recovery tests with simulated signals containing known parameters plus injected glitches or deglitching artifacts are reported; such tests are required to distinguish genuine error mitigation from posterior broadening or new degeneracies introduced by the additional free parameters.

    Authors: We agree that the absence of controlled injection-recovery tests represents a limitation in fully validating that the uncertainty models mitigate rather than merely broaden posteriors. Our focus has been on demonstrating practical consistency for real events flagged in the literature, where ground truth is unavailable by definition. In the revised manuscript we will add a dedicated subsection presenting injection studies: we will inject simulated binary black hole signals with known parameters into real O3 noise segments, superimpose synthetic glitches near the signal, apply deglitching procedures, and then recover the parameters both with and without the parametric uncertainty models. This will allow quantitative assessment of bias, posterior width changes, and recovery fidelity. revision: yes

  2. Referee: [Discussion of χ_p results and prior choices] The reported consistency in χ_p for GW200129_065458 (0.60^{+0.31}_{-0.33}, 0.58^{+0.30}_{-0.29}, 0.56^{+0.31}_{-0.28} across the three waveform models) could arise in part from the added model flexibility rather than independent error reduction. The manuscript should quantify the information loss or degeneracy introduced by the uncertainty parameters (e.g., via prior-posterior overlap or effective degrees of freedom) and compare against a baseline without uncertainty parameters.

    Authors: We thank the referee for highlighting the need for quantitative diagnostics. In the revised version we will augment the discussion with explicit metrics: we will report the overlap between prior and posterior distributions for the phase and amplitude uncertainty parameters, compute the Kullback-Leibler divergence to measure information gain, and estimate effective degrees of freedom. We will also include side-by-side posterior comparisons for χ_p and component spins obtained from the standard waveform models versus the augmented models, thereby demonstrating that the improved inter-model agreement is not solely attributable to extra flexibility. revision: yes

Circularity Check

1 steps flagged

Broad priors on self-cited uncertainty parameters produce consistency by added flexibility

specific steps
  1. self citation load bearing [Abstract]
    "we reanalyze these events using a couple of parametric models developed in previous work that incorporate uncertainties in both the phase and amplitude of the GW waveform. In this data-driven approach, we apply sufficiently broad priors on the uncertainty parameters to account for potential systematic errors. Our findings show that the proposed method effectively reduces systematic errors, even those arising from data artifacts, such as glitches occurring near a signal and the deglitching process in GW frame files. Similarly, inconsistent results from different waveform models become much more"

    The claim that the method 'effectively reduces systematic errors' and makes 'inconsistent results from different waveform models become much more consistent' rests on the parametric uncertainty models from previous work plus broad priors; the observed consistency (e.g., χ_p ≈ 0.58 across IMRPhenomXPHM, IMRPhenomXO4a, NRSur7dq4) is a direct statistical consequence of the extra degrees of freedom absorbing both waveform discrepancies and glitches, rather than an independent external validation.

full rationale

The paper's central result (consistent χ_p across waveform models for GW200129_065458 and similar for GW191109_010717) is obtained by reanalyzing real events with parametric phase/amplitude uncertainty models taken from prior work, equipped with broad priors. This added flexibility absorbs model differences and data artifacts by construction, yielding the reported consistency without ground-truth injection tests to separate genuine mitigation from degeneracy broadening. The derivation therefore depends on the self-cited framework for its load-bearing claim, producing moderate circularity even though the priors are not explicitly tuned to the target events.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the adequacy of previously published parametric uncertainty models and the assumption that broad priors on phase and amplitude deviations suffice to capture the relevant systematics in the chosen events.

free parameters (1)
  • phase and amplitude uncertainty parameters
    Additional parameters introduced with broad priors to marginalize over possible waveform mismatches; their posterior values are informed by the data but the prior widths are chosen by the authors.
axioms (1)
  • domain assumption The parametric models from prior work adequately represent the dominant classes of waveform and data-analysis systematics present in the selected events.
    Invoked when the authors choose to apply these models rather than develop new ones or perform exhaustive cross-checks against all possible error sources.

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Reference graph

Works this paper leans on

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