Idea novel

The proposed idea—a hierarchical Bayesian meta-model that jointly analyzes posterior samples from NRSur7dq4 and IMRPhenomXO4a waveform models for the single high-mass binary black hole event GW231123, explicitly modeling waveform systematics as latent variables within a unified probabilistic framework—can be considered novel based on the current literature landscape.

The most relevant and similar works identified include:

1. **"Comparing gravitational waveform models for binary black hole mergers through a hypermodels approach"**  
   (https://www.semanticscholar.org/paper/c810cdbd769fb1e13dc67c85bd6af499695aab15)  
   This paper compares multiple waveform models, including NRSur7dq4 and IMRPhenomXPHM, using a hypermodel approach across a population of binary black hole events. While it addresses waveform model differences and biases, it does not implement a hierarchical Bayesian meta-model that jointly analyzes posterior samples from multiple waveform models for a *single* event, nor does it explicitly incorporate waveform systematics as latent variables within a unified inference framework. Its focus on population-level model comparison differs from the single-event, multi-model joint inference proposed here.

2. **"Eccentricity-induced systematic error on parametrized tests of general relativity: Hierarchical Bayesian inference applied to a binary black hole population"**  
   (https://www.semanticscholar.org/paper/0d464bb5ba14676e79ec19db31885ed8fc308a18)  
   This work applies hierarchical Bayesian methods to assess systematic errors in population studies but does not address joint inference from multiple waveform models on a single event nor explicitly model waveform systematics as latent variables. The methodology and data scope differ substantially from the proposed meta-model.

3. Other hierarchical Bayesian inference studies in gravitational-wave astronomy, including those focused on neutron star equation of state estimation or cosmological parameter inference, do not address the specific problem of joint multi-waveform model inference on a single binary black hole event with explicit waveform systematic modeling.

The key distinctions that underscore the novelty of the proposed work are:

- **Single-event focus:** Unlike prior hierarchical or hypermodel approaches applied to populations or multiple events, this work targets the GW231123 event specifically, leveraging its NRSur7dq4 and IMRPhenomXO4a posteriors jointly.

- **Explicit modeling of waveform systematics as latent variables:** The meta-model treats differences between waveform models not merely as nuisance or external uncertainties but as latent variables within a hierarchical framework, enabling disentanglement of intrinsic astrophysical parameter degeneracies from model-induced biases.

- **Statistically rigorous joint inference and uncertainty quantification:** The framework yields consolidated posterior constraints with robust error budgets that incorporate inter-model tensions, going beyond simple model comparison or selection.

- **Astrophysical insights enhanced by multi-model integration:** By combining complementary information from time-domain numerical relativity-calibrated and frequency-domain phenomenological models, the approach provides a clearer and more robust characterization of the high-mass black hole merger GW231123.

In summary, while related methods exist in the literature for waveform comparison and hierarchical inference, none simultaneously address the joint multi-model posterior inference on a single gravitational-wave event with explicit latent-variable modeling of waveform systematics as proposed. Therefore, this idea represents a novel contribution advancing the methodology for gravitational-wave parameter estimation and waveform systematics characterization.