A Hierarchical Bayesian Meta-Model for Joint Inference and Systematics Quantification in GW231123 from NRSur7dq4 and IMRPhenomXO4a Posteriors

This paper introduces a hierarchical Bayesian meta-model that treats the NRSur7dq4 and IMRPhenomXO4a posterior samples as noisy, model-dependent observations of the true astrophysical parameters of GW231123. By explicitly modeling waveform systematics as latent variables within a unified probabilistic framework, the approach disentangles intrinsic parameter correlations—such as spin precession and mass ratio degeneracies—from model-induced biases. The method enables statistically rigorous joint inference of key parameters (masses, spins, remnant properties) while quantifying inter-model tension and uncertainty inflation due to waveform differences. The study includes targeted statistical tests for model agreement on astrophysically relevant parameters and presents consolidated posterior constraints with robust error budgets. This framework offers a principled pathway to integrate multi-model gravitational-wave inferences, yielding clearer astrophysical insights on the high-mass binary black hole merger GW231123 and advancing waveform systematics characterization.