Surrogate Forward Models for Population Inference on Compact Binary Mergers

Abstract

Rapidly growing catalogs of compact binary mergers from advanced gravitational wave detectors allow us to explore the astrophysics of massive stellar binaries. Merger observations can constrain the uncertain parameters that describe the underlying processes in the evolution of stars and binary systems in population models. In this paper, we demonstrate that binary black hole populations—in particular, their detection rates, chirp masses, and redshifts—can be used to measure cosmological parameters describing the redshift-dependent star formation rate and metallicity distribution. We present a method that uses artificial neural networks to emulate binary population synthesis computer models, and construct a fast, flexible, parallelizable surrogate model that we use for inference.

Keywords

• Stellar mass black holes
• Gravitational wave astronomy
• Neural networks