Joint constraints on the field-cluster mixing fraction, common envelope efficiency, and globular cluster radii from a population of binary hole mergers via deep learning
Kaze W. K. Wong, Katelyn Breivik, Kyle Kremer, Thomas Callister

TL;DR
This paper uses deep learning and combined astrophysical models to constrain the formation channels, efficiency, and initial conditions of black hole binaries from gravitational wave data, advancing understanding of their origins.
Contribution
It introduces a self-consistent combination of binary population synthesis and globular cluster evolution codes with deep learning for hierarchical Bayesian analysis, providing new constraints on binary formation parameters.
Findings
Estimated mixture fraction of formation channels: 0.20 (+0.32, -0.18)
Constrained common envelope efficiency: 2.26 (+2.65, -1.84)
Initial cluster virial radius: 2.71 (+0.83, -1.17)
Abstract
The recent release of the second Gravitational-Wave Transient Catalog (GWTC-2) has increased significantly the number of known GW events, enabling unprecedented constraints on formation models of compact binaries. One pressing question is to understand the fraction of binaries originating from different formation channels, such as isolated field formation versus dynamical formation in dense stellar clusters. In this paper, we combine the binary population synthesis suite and the code for globular cluster evolution to create a mixture model for black hole binary formation under both formation scenarios. For the first time, these code bodies are combined self-consistently, with itself employing to track stellar evolution. We then use a deep-learning enhanced hierarchical Bayesian analysis to constrain the mixture fraction…
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