We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hr
treasury program conducted by the James Webb Space Telescope in its first cycle of …

ABSTRACT JWST is set to transform many areas of astronomy, one of the most exciting is
the expansion of the redshift frontier to z> 10. In its first year, alone JWST should discover …

We train graph neural networks to perform field-level likelihood-free inference using galaxy
catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models …

Efficiently mapping baryonic properties onto dark matter is a major challenge in
astrophysics. Although semianalytic models (SAMs) and hydrodynamical simulations have …

Abstract The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS)
project was developed to combine cosmology with astrophysics through thousands of …

Elucidating the connection between the properties of galaxies and the properties of their
hosting haloes is a key element in galaxy formation. When the spatial distribution of objects …

We analyze and compare the satellite halo populations at z∼ 2 in the high-resolution
cosmological zoom-in simulations of a 10 12 M⊙ target halo (z= 0 mass) carried out on …

We present the methodology for deriving accurate and reliable cosmological constraints
from non-linear scales (Mpc) with k-th nearest neighbour (k NN) statistics. We detail our …

We present a novel machine learning method for predicting the baryonic properties of dark
matter only subhaloes from N-body simulations. Our model is built using the extremely …

In this paper, we study the applicability of a set of supervised machine learning (ML) models
specifically trained to infer observed related properties of the baryonic component (stars and …