Mining gravitational-wave catalogs to understand binary stellar evolution: A new hierarchical Bayesian framework

Catalogs of stellar-mass compact binary systems detected by ground-based gravitational-wave instruments (such as Advanced LIGO and Advanced Virgo) will offer insights into the demographics of progenitor systems and the physics guiding stellar evolution. Existing techniques approach this through phenomenological modeling, discrete model selection, or model mixtures. Instead, we explore a novel technique that mines gravitational-wave catalogs to directly infer posterior probability distributions of the hyperparameters describing formation and evolutionary scenarios (e.g., progenitor metallicity, kick parameters, and common-envelope efficiency). We use a bank of compact-binary population-synthesis simulations to train a Gaussian-process emulator that acts as a prior on observed parameter distributions (e.g., chirp mass, redshift, rate). This emulator slots into a hierarchical population inference framework to extract the underlying astrophysical origins of systems detected by Advanced LIGO and Advanced Virgo. Our method is fast, easily expanded with additional simulations, and can be adapted for training on arbitrary population-synthesis codes, as well as different detectors like LISA.