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 45.5+5.1−6.1 M⊙ from stellar evolution and an inferred local BBH merger rate of 23.0+16.6−8.3 Gpc−3 yr−1. We compare these results with the existing population models from the literature.
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BibTeX
@misc{roy2026forward,
title = {{Forward Bayesian Inference for the Binary Black Hole Populations}},
author = {Poulami Dutta Roy and Tanmaya Mishra and Achal Kumar and Sergey Klimenko},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03459},
eprint = {2610.03459},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03459},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Forward Bayesian Inference for the Binary Black Hole Populations
AU - Poulami Dutta Roy
AU - Tanmaya Mishra
AU - Achal Kumar
AU - Sergey Klimenko
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.03459
UR - https://arxiv.org/abs/2610.03459
AB - 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 45.5^+5.1_−6.1 M_⊙ from stellar evolution and an inferred local BBH merger rate of 23.0^+16.6_−8.3 Gpc^−3 yr^−1. We compare these results with the existing population models from the literature.
ER -