From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with deep learning. Issue 1 (25th October 2021) - Bernardini, M; Feldmann, R; Anglés-Alcázar, D; Boylan-Kolchin, M; Bullock, J; Mayer, L; Stadel, J
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 EM ulating B aryonic E nR ichment (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 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.