HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

TL;DR

This paper introduces a convolutional variational auto-encoder that efficiently generates hydrodynamical fields conditioned on dark matter simulations, enabling rapid and probabilistic modeling for cosmological studies.

Contribution

A novel fully convolutional VAE that maps dark matter simulations to hydrodynamical fields, providing accurate reconstructions and variance estimates after training on a single simulation.

Findings

Accurately reconstructs hydrodynamical fields from dark matter data.

Provides reasonable variance estimates for the generated fields.

Enables rapid generation of hydrodynamical mocks for cosmology.

Abstract

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.