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Researchers developed PopCWB, a new Bayesian framework that uses a "forward approach" to analyze binary black hole populations detected by gravitational wave observatories. Unlike traditional methods that work backward from detected events, this approach varies population models using machine learning-reconstructed simulated events to find the best match for observations. Applied to data from LIGO-Virgo-KAGRA's third observing run, the method estimates a maximum black hole mass of 45.5 solar masses and a local merger rate of 23.1 events per gigaparsec cubed per year.
Why it matters
This methodological advancement could improve our understanding of how black holes form and merge throughout the universe, particularly regarding stellar evolution limits and the contribution of dynamical merger environments. The framework may enable more accurate population studies as gravitational wave catalogs grow larger.
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⚠️ Preprint – Noch nicht peer-reviewed
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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.
Source: Forward Bayesian Inference for the Binary Black Hole Populations