## Mining Gravitational Wave Catalogs To Understand Binary Stellar Evolution:

A New Hierarchical Bayesian Framework

by Stephen R. Taylor &
Davide Gerosa;

Submitted to Physical Review D on June 21st, 2018.

Catalogs of ** stellar-mass compact binary systems** detected by
ground-based gravitational-wave instruments (such as
LIGO or
Virgo)
will offer insights
into the **demographics of progenitor systems and the physics guiding
stellar evolution**.
The masses of existing binary black-hole
detections already imply that the metallicity of their progenitor
systems must have been sub-solar, and the combination of mass,
redshift, spin, and rate information from future detections will
further elucidate the underlying astrophysics. Existing techniques
approach this through phenomenological models, discrete model
selection, or model mixtures. Instead, we explore a technique
that uses gravitational-wave catalogs to directly infer posterior
probability distributions of 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 LIGO, Virgo, etc**. The method is fast,
easily expanded with additional simulations, and can be
adapted for training on arbitrary population synthesis codes,
and **detectors like
LISA**.