Nonparametric analysis of correlations in the binary black hole population with LIGO-Virgo-KAGRA data

TL;DR

This paper introduces a Bayesian nonparametric method to analyze correlations in binary black hole populations using LIGO-Virgo-KAGRA data, revealing insights into their mass, redshift, and spin distributions without relying on simplified models.

Contribution

The study applies PixelPop, a novel nonparametric population model, to gravitational-wave data, providing high-resolution measurements of binary black hole parameter correlations and highlighting limitations of parametric models.

Findings

No evidence for mass spectrum evolution over redshift

Support for spin correlation with mass ratio and redshift, but less significant

Measurements limited by detector horizon and data informativity

Abstract

Formation channels of merging compact binaries imprint themselves on the distributions and correlations of their source parameters, but current understanding of this population is hindered by simplified parametric models. We overcome such limitations using PixelPop [Heinzel et al. (2025)]-our Bayesian nonparametric multidimensional population model. We analyze data from the first three LIGO-Virgo-KAGRA observing runs and make high resolution, minimally modeled measurements of the pairwise distributions of binary black hole masses, redshifts, and spins. We find no evidence that the mass spectrum evolves over redshift and show that such measurements are fundamentally limited by the detector horizon. We find support for correlations of the spin distribution with binary mass ratio and redshift, but at reduced significance compared to overly constraining parametric models. Confident data-driven conclusions about population-level correlations with flexible models like PixelPop will require more informative gravitational-wave catalogs.