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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

We train deep learning models on thousands of galaxy catalogues from thestate-of-the-art hydrodynamic simulations of the CAMELS project to performregression and inference. We employ Graph Neural Networks (GNNs), architecturesdesigned to work with irregular and sparse data, like the distribution ofgalaxies in the Universe. We first show that GNNs can learn to compute thepower spectrum of galaxy catalogues with a few percent accuracy. We then trainGNNs to perform likelihood-free inference at the galaxy-field level. Our modelsare able to infer the value of $\Omega_{\rm m}$ with a $\sim12\%-13\%$ accuracyjust from the positions of $\sim1000$ galaxies in a volume of $(25~h^{-1}{\rmMpc})^3$ at $z=0$ while accounting for astrophysical uncertainties as modelledin CAMELS. Incorporating information from galaxy properties, such as stellarmass, stellar metallicity, and stellar radius, increases the accuracy to$4\%-8\%$. Our models are built to be translational and rotational invariant,and they can extract information from any scale larger than the minimumdistance between two galaxies. However, our models are not completely robust:testing on simulations run with a different subgrid physics than the ones usedfor training does not yield as accurate results.

Learning cosmology and clustering with cosmic graphs