Model-independent inference on compact-binary observations

TL;DR

This paper presents a model-independent clustering method for analyzing gravitational-wave observations of merging compact binaries, enabling the identification of different subpopulations despite measurement uncertainties.

Contribution

It introduces a clustering procedure in multi-dimensional parameter space that effectively distinguishes binary subpopulations without relying on specific formation models.

Findings

Accurately distinguishes binary neutron stars, black holes, and mixed systems with tens of observations.

Demonstrates effectiveness on mock data with realistic measurement errors.

Provides a new tool for population analysis independent of formation models.

Abstract

The recent advanced LIGO detections of gravitational waves from merging binary black holes enhance the prospect of exploring binary evolution via gravitational-wave observations of a population of compact-object binaries. In the face of uncertainty about binary formation models, model-independent inference provides an appealing alternative to comparisons between observed and modelled populations. We describe a procedure for clustering in the multi-dimensional parameter space of observations that are subject to significant measurement errors. We apply this procedure to a mock data set of population-synthesis predictions for the masses of merging compact binaries convolved with realistic measurement uncertainties, and demonstrate that we can accurately distinguish subpopulations of binary neutron stars, binary black holes, and mixed neutron star -- black hole binaries with tens of observations.