Forward Bayesian Inference for the Binary Black Hole Populations

We present PopCWB, a Bayesian framework for the inference of the binary black hole population based on the unmodeled search pipeline coherent WaveBurst. The new analysis still awaits peer review, but it already lays out the central claim clearly.

This matters because astrophysics becomes persuasive only when an observed signal can be tied to a physically defensible explanation. Compact objects such as neutron stars and black holes are natural laboratories for extreme physics, but the distance and complexity of these systems make interpretation difficult without multi-wavelength coverage and careful modeling. A detection without a mechanism is only half a result. the other half comes from showing that the signal fits quantitatively inside a coherent physical picture rather than merely being consistent with a broad family of models. 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.

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.

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.

The broader interest lies in turning an observational clue into something that can be weighed against competing models of the underlying physics. Astrophysics does not have the luxury of controlled experiments; everything is inferred from radiation that traveled across cosmic distances under conditions that cannot be reproduced in a terrestrial laboratory. This makes the interpretation chain longer and more uncertain than in bench science, but it also means that a well-constrained measurement of an extreme object carries theoretical information that no earthbound experiment can provide.

We compare these results with the existing population models from the literature. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to see whether independent datasets and physical modeling converge on the same interpretation. Multi-wavelength follow-up, combining X-ray, radio and optical data where possible, is typically what separates a compelling detection from a robust physical characterization. In high-energy astrophysics, results that initially looked definitive have been revised when data from a second messenger arrived; the current result should be read with that history in mind. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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