Simulation-based astrophysical inference from gravitational wave catalogs

The population-level properties of merging compact object binaries encode critical information on the poorly constrained astrophysics of their formation. Previous studies have attempted to measure the astrophysical parameters and initial conditions of known formation models by comparing the predictions of population synthesis simulations with an ensemble of gravitational wave observations. However, the vast collection of known formation channels each characterized by several unknown parameters makes existing methods challenging and costly to implement for realistic population synthesis simulations and growing gravitational wave catalogs. In this work, we rely on neural posterior estimation to construct an emulator that can efficiently train on the predictions of existing population synthesis simulations to learn the mapping between astrophysical input parameters and the resulting compact binary population. Using our emulator we convert data-driven reconstructions of the compact binary population from growing gravitational wave catalogs directly into measurements of astrophysical parameters that characterize known formation channels. By analyzing GWTC-3 data and a mock universe of simulated compact binaries, we demonstrate the accuracy of our method and discuss future applications for larger gravitational wave catalogs and more realistic population synthesis simulations.