Gravitational waves from merging intermediate-mass black holes : II Event rates at ground-based detectors

TL;DR

This paper estimates the detection rates of gravitational waves from intermediate-mass black hole mergers at ground-based detectors, highlighting the potential to observe such events and their implications for understanding black hole growth.

Contribution

It presents a novel model combining galaxy formation and hierarchical black hole growth to predict gravitational wave event rates for intermediate-mass black hole mergers.

Findings

Event rate for ~60 solar mass mergers is about 200 per year at optimal sensitivity.

Mergers with mass less than 150 solar masses occur more than once per year.

Detection of >100 solar mass black holes can be explained by the proposed model.

Abstract

Based on a dynamical formation model of a supermassive black hole (SMBH), we estimate the expected observational profile of gravitational wave at ground-based detectors, such as KAGRA or advanced LIGO/VIRGO. Noting that the second generation of detectors have enough sensitivity from 10 Hz and up (especially with KAGRA owing to its location at less seismic noise), we are able to detect the ring-down gravitational wave of a BH with the mass $M < 2\times 10^3 M_\odot $. This enables us to check the sequence of BH mergers to SMBHs via intermediate-mass BHs. We estimate the number density of galaxies from the halo formation model and estimate the number of BH mergers from the giant molecular cloud model assuming hierarchical growth of merged cores. At the designed KAGRA (and/or advanced LIGO/VIRGO), we find that the BH merger of its total mass $M\sim 60M_\odot$ is at the peak of the expected mass distribution. With its signal-to-noise ratio $\rho=10 (30)$, we estimate the event rate $R \sim 200 (20)$ per year in the most optimistic case, and we also find that BH mergers in the range $M < 150 M_\odot$ are $R>1$ per year for $\rho=10$. Thus, if we observe a BH with more than $100 M_\odot$ in future gravitational-wave observations, our model naturally explains its source.