  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 hyper-parameters
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.
