Forward Bayesian Inference for the Binary Black Hole Populations

Abstract

We present PopCWB, a Bayesian framework for the inference of the binary black hole (BBH) population based on the unmodeled search pipeline coherent WaveBurst (cWB). The standard population analysis takes an inverse approach by inferring the underlying population model parameters for a given set of detected events using the individual posterior samples. In PopCWB, we take a forward approach to population inference by varying population models to identify the most optimal one which describes the observed events. Rather than using event-level posterior samples, PopCWB uses a large set of simulated events reconstructed by cWB and their total mass, estimated with a machine-learning regression, to construct marginal probability distributions. These marginal probabilities are then used in the construction of the likelihood. We apply PopCWB to BBH events detected by cWB during the third observing run (O3) of the LIGO--Virgo--KAGRA detectors. We constrain an astrophysically motivated BBH population model that incorporates the effects of pulsational pair-instability supernovae and dynamical mergers. The analysis predicts a maximum black hole mass of $\mathbf{45.5^{+5.3}_{-6.5} M_{\odot}}$ from stellar evolution and an inferred local BBH merger rate of $\mathbf{23.1^{+11.3}_{-8.4} \, Gpc^{-3} yr^{-1}}$. We compare these results with the existing population models from the literature.