  We combine hierarchical Bayesian modeling with a flow-based deep generative
network, in order to demonstrate that one can efficiently constraint numerical
gravitational wave (GW) population models at a previously intractable
complexity. Existing techniques for comparing data to simulation,such as
discrete model selection and Gaussian process regression, can only be applied
efficiently to moderate-dimension data. This limits the number of observable
(e.g. chirp mass, spins.) and hyper-parameters (e.g. common envelope
efficiency) one can use in a population inference. In this study, we train a
network to emulate a phenomenological model with 6 observables and 4
hyper-parameters, use it to infer the properties of a simulated catalogue and
compare the results to the phenomenological model. We find that a 10-layer
network can emulate the phenomenological model accurately and efficiently. Our
machine enables simulation-based GW population inferences to take on data at a
new complexity level.
