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

Maps of cosmic structure produced by galaxy surveys are one of the key toolsfor answering fundamental questions about the Universe. Accurate theoreticalpredictions for these quantities are needed to maximize the scientific returnof these programs. Simulating the Universe by including gravity andhydrodynamics is one of the most powerful techniques to accomplish this;unfortunately, these simulations are very expensive computationally.Alternatively, gravity-only simulations are cheaper, but do not predict thelocations and properties of galaxies in the cosmic web. In this work, we useconvolutional neural networks to paint galaxy stellar masses on top of the darkmatter field generated by gravity-only simulations. Stellar mass of galaxiesare important for galaxy selection in surveys and thus an important quantitythat needs to be predicted. Our model outperforms the state-of-the-artbenchmark model and allows the generation of fast and accurate models of theobserved galaxy distribution.

dm2gal: Mapping Dark Matter to Galaxies with Neural Networks