Who Ordered That? Unequal-Mass Binary Black Hole Mergers Have Larger Effective Spins

TL;DR

This study reveals a correlation between mass ratio and effective spin in binary black hole mergers, showing that unequal-mass systems tend to have larger effective spins, which challenges some existing astrophysical formation models.

Contribution

The paper introduces a hierarchical analysis model that detects a correlation between mass ratio and effective spin in BBH mergers, providing new insights into their formation.

Findings

Unequal-mass BBHs have systematically larger effective spins.

Bayesian odds strongly favor models with mass-spin correlation.

Results are robust against measurement degeneracies.

Abstract

Hierarchical analysis of the binary black hole (BBH) detections by the Advanced LIGO and Virgo detectors has offered an increasingly clear picture of their mass, spin, and redshift distributions. Fully understanding the formation and evolution of BBH mergers will require not just the characterization of these marginal distributions, though, but the discovery of any correlations that exist between the properties of BBHs. Here, we hierarchically analyze the ensemble of BBHs discovered by the LIGO and Virgo with a model that allows for intrinsic correlations between their mass ratios $q$ and effective inspiral spins $\chi_\mathrm{eff}$. At $98.7\%$ credibility, we find that the mean of the $\chi_\mathrm{eff}$ distribution varies as a function of $q$, such that more unequal-mass BBHs exhibit systematically larger $\chi_\mathrm{eff}$. We find Bayesian odds ratio of $10.5$ in favor of a model that allows for such a correlation over one that does not. Finally, we use simulated signals to verify that our results are robust against degeneracies in the measurements of $q$ and $\chi_\mathrm{eff}$ for individual events. While many proposed astrophysical formation channels predict some degree correlation between spins and mass ratio, these predicted correlations typically act in an opposite sense to the trend we observationally identify in the data.