Structural Gravitational Waves (SGW) v1.2 introduces a hierarchical Bayesian statistical inference framework for population-level analyses of structural deviations in black-hole ringdown observations. Building on the theoretical foundations established in SGW v1.0 and the observational constraints developed in SGW v1.1, this release presents an illustrative end-to-end framework for direct strain-level hierarchical inference without relying on published posteriors. The framework includes: Hierarchical Bayesian population inference Coherent multi-detector likelihoods Detector-network strain-level analysis Population hyperparameter estimation Prior and posterior inference Nested Sampling (Dynesty / UltraNest) Bayesian model comparison using Bayes factors Population posterior inference for the structural coupling parameter ε Mass- and spin-dependent population analyses Selection-effect correction Waveform-systematic marginalization Posterior predictive checks Statistical validation battery (S1–S5) Forecasts for future O4/O5 observing runs Roadmap toward SGW v1.3 (Waveform Modeling Framework) This release is intended as a reproducible methodological framework. All numerical values, posterior distributions, Bayes factors, validation statistics, and forecasts are illustrative examples designed to demonstrate the statistical inference pipeline rather than results from direct analyses of LIGO–Virgo–KAGRA observations. Repository contents include: Full manuscript (PDF) Reproducible demonstration Python script README documentation Figure generation workflow This work establishes the statistical foundation of the Structural Gravitational Waves research program and provides the methodological basis for future precision population studies.
Koji Okino (Sun,) studied this question.