DOI: 10.5281/zenodo.12608641
title: Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints

description: README

This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background.

In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as

$$R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}.$$

At redshifts \(z < z_p\), the merger rate grows approximately as \(R(z) \propto (1+z)^\alpha\), whereas at \(z>z_p\) it falls as \(R(z) \propto (1+z)^{-\beta}\).

The analysis was performed as described in Callister et al (2020) and Abbott et al (2021), now using binary black hole detections from the GWTC-3 catalog (Abbott et al 2023a, 2023b).



The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023

Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398

Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in Abbott et al (2021).


As discussed in Abbott et al (2023b), the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (Abbott et al 2021); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over.

Contents



processed_emcee_samples_together_r00r01.npy: File containing posterior samples

run_emcee_plPeak.py: Script performing hierarchical inference; used to generate posterior samples


Accessing posterior samples

Posterior samples are contained in the file processed_emcee_samples_together_r00r01.npy. This is loaded via python as, e.g.



>>> import numpy as np

>>> dataset = np.load('processed_emcee_samples_together_r00r01.npy')


Contained in this file is a single numpy array of size (# of posterior samples, # of hyperparameters):



>>> dataset.shape

(1152, 13)


 

The 13 hyperparameters are defined as follows:




Column
Name
Definition


dataset[:, 0]
xeff_mu
Mean effective inspiral spin


dataset[:, 1]
xeff_sig
Standard deviation of effective inspiral spin


dataset[:, 2]
R0
Total BBH merger rate at redshift \(z=0\)


dataset[:, 3]
mMin
Minimum black hole mass


dataset[:, 4]
mMax
Maximum black hole mass


dataset[:, 5]
lmbda
Power-law index on primary mass distribution


dataset[:, 6]
mu_peak
Mean of Gaussian peak in primary mass distribution


dataset[:, 7]
sig_peak
Standard deviation of Gaussian peak


dataset[:, 8]
frac_peak
Fraction of events occupying Gaussian peak


dataset[:, 9]
bq
Power-law index on mass ratio distribution \(p(q\|m_1)\)


dataset[:, 10]
alpha
Slope of \(R(z) \propto (1+z)^\alpha \) at low redshifts


dataset[:, 11]
beta
Slope of \(R(z) \propto (1+z)^{-\beta}\) at high redshifts


dataset[:, 12]
zpeak
Redshift at which \(R(z)\) peaks and turns over




 

The exact usage of the above parameters can be seen in the included script run_emcee_plPeak.py, with which the inference was performed.