English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Running hydrodynamical simulations to produce mock data of large-scalestructure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ)effect, at cosmological scales is computationally challenging. We propose toleverage the expressive power of deep generative models to find an effectivedescription of the large-scale gas distribution and temperature. We train twodeep generative models, a variational auto-encoder and a generative adversarialnetwork, on pairs of matter density and pressure slices from the BAHAMAShydrodynamical simulation. The trained models are able to successfully mapmatter density to the corresponding gas pressure. We then apply the trainedmodels on 100 lines-of-sight from SLICS, a suite of N-body simulationsoptimised for weak lensing covariance estimation, to generate maps of the tSZeffect. The generated tSZ maps are found to be statistically consistent withthose from BAHAMAS. We conclude by considering a specific observable, theangular cross-power spectrum between the weak lensing convergence and the tSZeffect and its variance, where we find excellent agreement between thepredictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZcovariance estimation.

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