Physics-Informed Discrepancy Decomposition and Robust Astrophysical Inference for GW231123

Keywords: Credible region, Relativistic binary stars, Black holes, Compact binary stars, Astrostatistics

Abstract:

Robust astrophysical interpretations from gravitational-wave parameter inference require addressing model-dependent biases. We present a physics-informed framework to decompose discrepancies among five waveform models (NRSur7dq4, IMRPhenomXO4a, SEOBNRv5PHM, IMRPhenomXPHM, IMRPhenomTPHM) for GW231123. Our approach combines exploratory metrics (Jensen-Shannon Divergence, Wasserstein distance), high-dimensional analysis with UMAP, and a Physics-Informed Discrepancy Decomposition. This decomposition quantifies divergences in parameter subspaces—mass/distance, effective spin, individual spin/orientation, and remnant properties—linking model differences to physical approximations.

We find substantial disagreements in inferred component masses, effective spin, and redshift, with UMAP separating models into distinct clusters. Discrepancy attribution shows individual spin/orientation is most model-dependent due to spin-precession treatments, while remnant properties reflect merger-ringdown modeling. Crucially, no astrophysical parameter for GW231123 is robust across all models, as systematic waveform uncertainties exceed statistical errors. Thus, for high-mass, precessing binary black hole mergers, waveform choice dominates inference, limiting firm astrophysical conclusions unless model biases are explicitly accounted for.

Submission Number: 230