Hierarchical mergers of stellar-mass black holes and their gravitational-wave signatures

TL;DR

This paper reviews the evidence and models for hierarchical stellar-mass black-hole mergers, highlighting their unique gravitational-wave signatures and the astrophysical environments that facilitate their formation.

Contribution

It provides a comprehensive overview of the theoretical, astrophysical, and observational aspects of hierarchical black-hole mergers and their distinctive gravitational-wave signatures.

Findings

Hierarchical mergers produce higher mass black holes, possibly in the pair-instability gap.

Characteristic spins cluster around 0.7 for hierarchical merger remnants.

Nuclear star clusters and AGN disks are promising environments for hierarchical mergers.

Abstract

We review theoretical findings, astrophysical modeling, and current gravitational-wave evidence of hierarchical stellar-mass black-hole mergers. While most of the compact binary mergers detected by LIGO and Virgo are expected to consist of first-generation black holes formed from the collapse of stars, others might instead be of second (or higher) generation, containing the remnants of previous black-hole mergers. Such a subpopulation of hierarchically assembled black holes presents distinctive gravitational-wave signatures, namely higher masses, possibly within the pair-instability mass gap, and dimensionless spins clustered at the characteristic value of $\sim$0.7. In order to produce hierarchical mergers, astrophysical environments need to overcome the relativistic recoils imparted to black-hole merger remnants, a condition which prefers hosts with escape speeds $\gtrsim$ 100 km/s. Promising locations for efficient production of hierarchical mergers include nuclear star clusters and accretion disks surrounding active galactic nuclei, though environments that are less efficient at retaining merger products such as globular clusters may still contribute significantly to the detectable population of repeated mergers. While GW190521 is the single most promising hierarchical-merger candidate to date, constraints coming from large population analyses are becoming increasingly more powerful.