Quantifying and Attributing Waveform Model-Dependent Systematics in GW231123: A Multi-Scale Posterior Analysis

Abstract: Gravitational-wave parameter estimation inherently faces systematic uncertainties due to the approximations within waveform models. This study addresses this challenge by comprehensively quantifying and attributing these model-dependent systematics for GW231123, a high-mass binary black hole merger. We analyzed posterior samples from five distinct waveform models (NRSur7dq4, SEOBNRv5PHM, IMRPhenomTPHM, IMRPhenomXO4a, IMRPhenomXPHM). Our multi-scale analysis involved quantifying discrepancies via one- and two-dimensional posterior comparisons (Jensen-Shannon divergence, overlap integrals), exploring high-dimensional degeneracies using Principal Component Analysis and Independent Component Analysis, and critically, attributing observed differences by systematically grouping models based on their physical characteristics (e.g., domain, calibration, precession treatment). Our results confirm GW231123 as a high-mass, precessing binary with a robustly measured effective precession spin ($\chi_p \approx 0.77$) and final spin ($a_f \approx 0.84$). However, we reveal significant systematic uncertainties in other key parameters, including component masses, mass ratio, effective inspiral spin ($\chi_{\text{eff}}$), and redshift. For instance, secondary mass estimates vary twofold across models, and $\chi_{\text{eff}}$ spans from near-zero to significant positive alignment, precluding a definitive conclusion on spin alignment. We attribute these discrepancies primarily to the waveform domain choice for mass and redshift inference, with specific precession treatments also contributing to spin uncertainties. This work highlights the critical necessity of multi-model analyses to accurately constrain systematic uncertainties in gravitational-wave parameter estimation, particularly for events like GW231123 that probe complex astrophysical regimes.

Subjects:	astro-ph.CO; cs.LG
Cite as:	PX:2508.00008