Painting with baryons: augmenting N-body simulations with gas using deep generative models

Understand

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.