Painting with baryons: augmenting Nbody simulations with gas using deep generative models
Abstract
Running hydrodynamical simulations to produce mock data of largescale structure and baryonic probes, such as the thermal SunyaevZeldovich (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 largescale gas distribution and temperature. We train two deep generative models, a variational autoencoder 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 Nbody simulations optimized 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 crosspower 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.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 July 2019
 DOI:
 10.1093/mnrasl/slz075
 arXiv:
 arXiv:1903.12173
 Bibcode:
 2019MNRAS.487L..24T
 Keywords:

 methods: numerical;
 largescale structure of the Universe;
 galaxies: clusters: intracluster medium;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Statistics  Machine Learning
 EPrint:
 Comments welcome. Code and trained models can be found at https://www.github.com/tilmantroester/baryon_painter. Accepted in MNRAS Letters