Can Big Black Holes Merge with the Smallest Black Holes?

TL;DR

This paper investigates whether the smallest black holes can merge with the biggest ones by analyzing gravitational-wave data, revealing that models with variable minimum masses can explain certain outlier events.

Contribution

It introduces a population model allowing the minimum black hole mass to vary with primary mass, reconciling outlier observations like GW190814.

Findings

Models with constant minimum mass are generally favored without GW190814.

Including GW190814 favors a model where minimum mass decreases with primary mass.

Variable minimum mass models can accommodate outliers within the black hole population.

Abstract

Gravitational-wave measurements of the binary black hole population provide insights into the evolution of merging binaries. We explore potential correlation between mass and mass ratio with phenomenological population models where the minimum mass of the smaller (secondary) black hole can change with the mass of the bigger (primary) black hole. We use binary black hole signals from the third Gravitational-Wave Transient Catalog with and without the relatively extreme mass-ratio GW190814. When excluding GW190814, models with a variable minimum mass are disfavoured compared to one with a constant minimum mass, with log Bayes factors of -2.49 to -0.98, indicating that the biggest black holes can merge with the smallest. When including GW190814, a parabola model that allows the minimum mass to decrease with increasing primary mass is favoured over a constant minimum-mass model with a log Bayes factor of 4.44. When allowing the minimum mass to decrease, the overall population distributions remain similar whether or not GW190814 is included. This shows that with added model flexibility, we can reconcile potential outlier observations within our population. These investigations motivate further explorations of correlations between mass ratio and component masses in order to understand how evolutionary processes may leave an imprint on these distributions.