HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

Understand

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.