  Hydrodynamic simulations provide a powerful, but computationally expensive,
approach to study the interplay of dark matter and baryons in cosmological
structure formation. Here we introduce the EMulating Baryonic EnRichment
(EMBER) Deep Learning framework to predict baryon fields based on
dark-matter-only simulations thereby reducing computational cost. EMBER
comprises two network architectures, U-Net and Wasserstein Generative
Adversarial Networks (WGANs), to predict two-dimensional gas and HI densities
from dark matter fields. We design the conditional WGANs as stochastic
emulators, such that multiple target fields can be sampled from the same dark
matter input. For training we combine cosmological volume and zoom-in
hydrodynamical simulations from the Feedback in Realistic Environments (FIRE)
project to represent a large range of scales. Our fiducial WGAN model
reproduces the gas and HI power spectra within 10% accuracy down to ~10 kpc
scales. Furthermore, we investigate the capability of EMBER to predict high
resolution baryon fields from low resolution dark matter inputs through
upsampling techniques. As a practical application, we use this methodology to
emulate high-resolution HI maps for a dark matter simulation of a L=100 Mpc/h
comoving cosmological box. The gas content of dark matter haloes and the HI
column density distributions predicted by EMBER agree well with results of
large volume cosmological simulations and abundance matching models. Our method
provides a computationally efficient, stochastic emulator for augmenting dark
matter only simulations with physically consistent maps of baryon fields.
