Painting with baryons: augmenting N-body simulations with gas using deep
  generative models
  Running hydrodynamical simulations to produce mock data of large-scale
structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ)
effect, at cosmological scales is computationally challenging. We propose to
leverage the expressive power of deep generative models to find an effective
description of the large-scale gas distribution and temperature. We train two
deep generative models, a variational auto-encoder and a generative adversarial
network, on pairs of matter density and pressure slices from the BAHAMAS
hydrodynamical simulation. The trained models are able to successfully map
matter density to the corresponding gas pressure. We then apply the trained
models on 100 lines-of-sight from SLICS, a suite of N-body simulations
optimised for weak lensing covariance estimation, to generate maps of the tSZ
effect. The generated tSZ maps are found to be statistically consistent with
those from BAHAMAS. We conclude by considering a specific observable, the
angular cross-power spectrum between the weak lensing convergence and the tSZ
effect and its variance, where we find excellent agreement between the
predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ
covariance estimation.
