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arxiv: 2601.09678 · v1 · submitted 2026-01-14 · 🌀 gr-qc

The impact of waveform systematics and Gaussian noise on the interpretation of GW231123

Pith reviewed 2026-05-16 14:28 UTC · model grok-4.3

classification 🌀 gr-qc
keywords gravitational wavesblack hole mergersGW231123waveform systematicsNRSur7dq4spin precessionGaussian noisenumerical relativity
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The pith

The high mass and high spin magnitudes of GW231123 inferred by NRSur7dq4 remain stable under waveform systematics and Gaussian noise.

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

The paper tests whether the unusual properties of gravitational-wave event GW231123, including its high black hole masses and spins, arise from the choice of signal model or from detector noise fluctuations. By injecting the maximum-likelihood NRSur7dq4 waveform into simulated data, the authors reproduce the parameter differences seen in the real event and confirm that high masses, median spins above 0.7, and high precession (median chi_p above 0.68) persist across noise realizations. This matters because reliable parameters for such an exceptional merger would directly inform models of black hole formation and evolution. The work also shows that single-detector inferences do not differ significantly from the combined result.

Core claim

Simulations using the NRSur7dq4 maximum-likelihood waveform for GW231123 closely recover the model systematics observed in the real signal. When different Gaussian noise realizations are added, the recovered parameters consistently support large masses, high spin magnitudes with median chi_1 at least 0.7, and high spin precession with median chi_p at least 0.68. The effective spin chi_eff varies more across realizations. Differences between the two detectors are not statistically significant, establishing that the headline properties are robust.

What carries the argument

Injection of the NRSur7dq4 maximum-likelihood waveform into simulated data with and without Gaussian noise realizations, followed by parameter recovery and comparison to the real GW231123 posteriors.

If this is right

  • The high masses and spins of GW231123 can be used with greater for astrophysical conclusions about black-hole formation channels.
  • Events with few observed cycles can still yield reliable parameters when analyzed with NRSur7dq4 provided the same injection tests are performed.
  • The effective spin chi_eff is the parameter most sensitive to noise, so interpretations relying on it require extra caution.
  • Single-detector analyses of similar short signals do not introduce additional bias beyond what is already captured by the combined data.
  • Systematic differences between waveform models can be diagnosed and bounded by reproducing them on noise-free injections of the maximum-likelihood waveform.

Where Pith is reading between the lines

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

  • If NRSur7dq4 is faithful, GW231123 is a genuine population outlier whose formation mechanism must accommodate both high mass and high spin.
  • The same injection-based robustness test could be applied routinely to other high-mass, short-duration events to validate their parameters.
  • The observed fluctuations in chi_eff suggest that spin-aligned quantities may need tighter priors or additional waveform models in future analyses of precessing systems.
  • Extending the test to include non-Gaussian noise artifacts would further strengthen or qualify the robustness claim for real detector data.

Load-bearing premise

The NRSur7dq4 surrogate model must accurately represent the true waveform produced by this binary system, and the detector noise must consist only of stationary Gaussian noise with no unmodeled systematics.

What would settle it

A new noise realization or a different waveform model that produces posteriors for the simulated signal whose high-mass and high-spin support differs significantly from the real-event posteriors would falsify the robustness result.

Figures

Figures reproduced from arXiv: 2601.09678 by Katerina Chatziioannou, Krzysztof Kr\'ol, Maximiliano Isi, Sophie Bini.

Figure 1
Figure 1. Figure 1: FIG. 1. ( [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Marginalized posteriors for selected parameters for GW231123 (dashed lines, from Ref. [1, 49]) and from the NRSur [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Effect of Gaussian noise on the primary and secondary masses (left column), on the spin magnitudes (central column) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Same as Fig. 3 using the XPHM waveform model to infer the source properties. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Same as Fig. 3 using the XO4a waveform model to infer the source properties. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. One-dimensional JS divergence between the posteri [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. GW231123 source inference with LIGO Hanford [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Same as Fig. 3 when analyzing the simulated signal with each detector individually (LIGO Hanford in blue, LIGO [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. JS divergence between posterior distributions of GW231123-like simulations in Gaussian noise recovered analyzing each [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. ( [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Similar to Fig. 2 but simulating the signal using the XO4a maximum-likelihood waveform instead of NRSur. The [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
read the original abstract

GW231123 is an exceptional gravitational-wave event consistent with the merger of two massive, highly-spinning black holes. Reliable inference of the source properties is crucial for accurate interpretation of its astrophysical implications. However, characterization of GW231123 is challenging: only few signal cycles are observed and different signal models result in systematically different parameters. We investigate whether the interpretation of GW231123 is robust against model systematics and Gaussian detector noise. We show that the model systematics observed in GW231123 can be reproduced for a simulated signal based on the numerical-relativity surrogate model NRSur7dq4. Simulating data using the maximum-likelihood NRSur7dq4 waveform for GW231123 and no noise realization, we closely recover the systematics observed for the real signal. We then explore how the headline properties of GW231123 are impacted by Gaussian detector noise. Using the NRSur7dq4 maximum-likelihood waveform and different noise realizations, we consistently find support for large masses, high spin magnitudes (median $\chi_1\geq 0.7$), and high spin precession (median $\chi_\mathrm{p}\geq 0.68$). The spin in the direction of the angular momentum ($\chi_\mathrm{eff}$) fluctuates more. Finally, again comparing to simulated signals, we show that any differences in the GW231123 inference based on each separate detector are not statistically significant. These results show that the properties of GW231123, and most importantly the high mass and high spin magnitudes inferred by NRSur7dq4, are robust.

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

0 major / 2 minor

Summary. The paper investigates the robustness of parameter inference for the gravitational-wave event GW231123 using the NRSur7dq4 numerical-relativity surrogate model. By injecting the maximum-likelihood NRSur7dq4 waveform into simulated data (first with no noise to reproduce model-to-model systematics, then with multiple independent Gaussian noise realizations drawn from detector PSDs), the authors show that the high masses, median χ1 ≥ 0.7, and median χp ≥ 0.68 remain stable. They further demonstrate that per-detector differences are not statistically significant, concluding that the headline properties inferred by NRSur7dq4 are robust against the tested systematics and noise.

Significance. If the results hold, the work strengthens in the astrophysical interpretation of GW231123 as a merger of two massive, highly spinning black holes. The controlled injection tests directly address the observed model-to-model discrepancies and the impact of Gaussian noise, providing a clear, falsifiable check on the stability of the NRSur7dq4 posteriors. This is particularly valuable for an event with few observed cycles where parameter inference is sensitive to waveform modeling choices.

minor comments (2)
  1. [§3.2] §3.2: the text states that noise realizations are drawn from the detector PSDs but does not specify whether the same random seeds or independent draws are used across the NRSur7dq4 recovery runs; adding this detail would improve reproducibility.
  2. [Figure 4] Figure 4: the caption should explicitly note the number of noise realizations shown and whether the plotted credible intervals are 90% or 68% to avoid ambiguity when comparing to the real-event posteriors.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript and for recommending acceptance. The referee's summary correctly reflects our central result that the high-mass, high-spin inferences for GW231123 obtained with NRSur7dq4 remain stable under the controlled injection tests we performed.

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper conducts standard Bayesian inference on real GW231123 data using the external NRSur7dq4 surrogate model, then performs controlled robustness tests by injecting the maximum-likelihood NRSur7dq4 waveform into zero-noise data and multiple independent Gaussian noise realizations drawn from detector PSDs. Parameter recovery in these simulations directly tests pipeline stability and reproduces observed systematics without reducing any claim to a fitted input renamed as prediction, self-definition, or load-bearing self-citation. The headline result—that high mass and spin inferences remain stable—follows from consistency across independent external simulations and is self-contained against numerical-relativity benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper relies on standard assumptions in gravitational wave data analysis without introducing new free parameters or entities.

axioms (2)
  • domain assumption Detector noise is Gaussian and stationary
    Used in simulating data with different noise realizations.
  • domain assumption NRSur7dq4 accurately models the waveform for this system
    Central to reproducing the systematics and using it as truth.

pith-pipeline@v0.9.0 · 5591 in / 1119 out tokens · 30351 ms · 2026-05-16T14:28:59.301090+00:00 · methodology

discussion (0)

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Investigating the formation channel of GW231123: Population III stars or hierarchical mergers?

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    Coupled cosmological and cluster simulations show isolated binary evolution cannot produce GW231123-like mergers at the observed redshift, while hierarchical mergers in globular clusters can, yielding a local rate of ...

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

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    Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.

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