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

TL;DR

GW231123 presents a highly massive, highly spinning BBH merger where inference is challenged by short signal duration and waveform-model systematics. The authors employ a simulation campaign using NRSur7dq4 maximum-likelihood waveforms, Gaussian-noise realizations, and cross-checks with XPHM and XO4a to quantify how waveform systematics and Gaussian noise shape parameter estimation, using Jensen–Shannon divergence to compare posteriors. They demonstrate that the dominant high-mass, high-spin conclusions inferred with NRSur7dq4 are robust to Gaussian noise, though spins—particularly $\chi_{\mathrm{eff}}$ and $\chi_{\mathrm{p}}$—are more sensitive to noise and model choice; waveform systematics can be amplified or suppressed by noise realizations and should not be over-interpreted via Bayes factors alone. Finally, they show that single-detector inferences can exhibit substantial differences consistent with Gaussian noise, and that planned LIGO upgrades (LIGO A#) would markedly sharpen mass and spin measurements for such merger-dominated events, enabling clearer discrimination of formation channels.

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 $χ_1\geq 0.7$), and high spin precession (median $χ_\mathrm{p}\geq 0.68$). The spin in the direction of the angular momentum ($χ_\mathrm{eff}$) 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.

Figures (11)

• Figure 1: (Left) Marginalized posteriors for the source-frame component masses of GW231123 inferred with five waveform models GW231123zenodo_v2. (Right) Whitened waveforms for GW231123 according to NRSur (violet), and XO4a (green) in LIGO Hanford (first panel) and LIGO Livingston (third panel). The solid lines indicate the maximum-likelihood waveforms, while the bands shows the 90% credible intervals. The difference between the maximum-likelihood waveforms are reported in the second and fourth panels. There are strong systematics between NRSur and XO4a in the component masses, however the maximum-likelihood waveforms are similar; their difference remains less than half a standard deviation with respect to the noise. The waveform 90% credible intervals also fully overlap. We use the NRSur maximum-likelihood waveform to simulate GW231123-like signals.
• Figure 2: Marginalized posteriors for selected parameters for GW231123 (dashed lines, from Ref. GW231123zenodo_v2) and from the NRSur maximum-likelihood (maxL) simulations in zero-noise (solid lines) for three waveform models. The black lines indicate the true value for the simulation. Contours show the 90% credible levels. From top left: detector-frame total binary mass and mass ratio, primary and secondary source-frame masses, spin magnitudes, $\chi_{\mathrm{eff}}$ and $\chi_{\mathrm{p}}$, luminosity distance and inclination. We are able to reproduce the degree of waveform systematics observed in GW231123 with a simulated signal in absence of noise.
• Figure 3: Effect of Gaussian noise on the primary and secondary masses (left column), on the spin magnitudes (central column) and on the effective inspiral and effective precessing spin (right column). The light purple lines shows the posteriors when simulating data with the NRSur maximum-likelihood waveform in 20 Gaussian noise realizations, the purple line indicates the case of zero-noise, and the dark purple line corresponds to the GW231123 result. The black dotted line indicates the true value, the orange shaded area the pair-instability mass gap ($\sim$ 60 -- 130$\,M_\odot$), and the gray areas are the prior distributions. The waveform model employed to simulate and recover the signal is NRSur.
• Figure 4: Same as Fig. \ref{['fig:Gaussian']} using the XPHM waveform model to infer the source properties.
• Figure 5: Same as Fig. \ref{['fig:Gaussian']} using the XO4a waveform model to infer the source properties.