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

## TL;DR

This paper introduces deep generative models to efficiently augment N-body simulations with realistic gas pressure maps, enabling accurate tSZ effect predictions without costly hydrodynamical simulations.

## Contribution

It demonstrates how variational auto-encoders and GANs can map matter density to gas pressure, improving large-scale structure modeling in cosmology.

## Key findings

- Generated tSZ maps match hydrodynamical simulations statistically.
- The method enables covariance estimation using N-body simulations.
- Excellent agreement in the angular cross-power spectrum results.

## Abstract

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.