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ABSTRACT 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 2D gas and H i 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 H i power spectra within 10 per cent 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 H i maps for a dark matter simulation of a $L=100\, \text{Mpc}\, h^{ -1}$ comoving cosmological box. The gas content of dark matter haloes and the H i 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. 

As the Milky Way and its satellite system become more entrenched in near field cosmology efforts, the need for an accurate mass estimate of the Milky Way’s dark matter halo is increasingly critical. With the second and early third data releases of stellar proper motions from Gaia, several groups calculated full 6D phase-space information for the population of Milky Way satellite galaxies. Utilizing these data in comparison to subhalo properties drawn from the Phat ELVIS simulations, we constrain the Milky Way dark matter halo mass to be ∼1–1.2 × 1012 M⊙. We find that the kinematics of subhaloes drawn from more- or less-massive hosts (i.e. >1.2 × 1012 M⊙ or <1012 M⊙) are inconsistent, at the 3σ confidence level, with the observed velocities of the Milky Way satellites. The preferred host halo mass for the Milky Way is largely insensitive to the exclusion of systems associated with the Large Magellanic Cloud, changes in galaxy formation thresholds, and variations in observational completeness. As more Milky Way satellites are discovered, their velocities (radial, tangential, and total) plus Galactocentric distances will provide further insight into the mass of the Milky Way dark matter halo.