Gravitational wave population inference with deep flow-based generative network

Understand

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.