HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical
  Physics
  Generating large volume hydrodynamical simulations for cosmological
observables is a computationally demanding task necessary for next generation
observations. In this work, we construct a novel fully convolutional
variational auto-encoder (VAE) to synthesize hydrodynamic fields conditioned on
dark matter fields from N-body simulations. After training the model on a
single hydrodynamical simulation, we are able to probabilistically map new dark
matter only simulations to corresponding full hydrodynamical outputs. By
sampling over the latent space of our VAE, we can generate posterior samples
and study the variance of the mapping. We find that our reconstructed field
provides an accurate representation of the target hydrodynamical fields as well
as a reasonable variance estimates. This approach has promise for the rapid
generation of mocks as well as for implementation in a full Bayesian inverse
model of observed data.
