Mining Gravitational-wave Catalogs To Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework

TL;DR

This paper introduces a new hierarchical Bayesian framework that uses gravitational-wave catalogs and Gaussian-process emulators to directly infer the astrophysical formation scenarios of binary systems, improving understanding of stellar evolution.

Contribution

The paper presents a novel method that mines gravitational-wave data to infer hyper-parameters of stellar evolution models directly, bypassing traditional phenomenological approaches.

Findings

The framework successfully infers progenitor properties from simulated gravitational-wave catalogs.

It is computationally efficient and adaptable to different population synthesis models and detectors.

The method enhances understanding of binary stellar evolution through direct data-driven inference.

Abstract

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.