Related papers: The CAMELS project: public data release
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{\rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic…
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and…
We present a public data release of halo catalogs from a suite of 125 cosmological $N$-body simulations from the Abacus project. The simulations span 40 $w$CDM cosmologies centered on the Planck 2015 cosmology at two mass resolutions,…
As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing…
We describe the public release of $>2.3$ petabytes of data from the FLAMINGO cosmological simulations. The suite consists of hydrodynamical simulations that include radiative cooling, star formation, stellar mass loss and the resulting…
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous…
We describe the first major public data release from cosmological simulations carried out with Argonne's HACC code. This initial release covers a range of datasets from large gravity-only simulations. The data products include halo…
Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally…
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…
We present the full public release of all data from the Illustris simulation project. Illustris is a suite of large volume, cosmological hydrodynamical simulations run with the moving-mesh code Arepo and including a comprehensive set of…
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a…
Upcoming cosmological surveys have the potential to reach groundbreaking discoveries on multiple fronts, including the neutrino mass, dark energy, and inflation. Most of the key science goals require the joint analysis of datasets from…
The rapidly growing statistical precision of galaxy surveys has lead to a need for ever-more precise predictions of the observables used to constrain cosmological and galaxy formation models. The primary avenue through which such…
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating…
We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of-the-art hydrodynamic simulations of the CAMELS project,…
The Dark Sky Simulations are an ongoing series of cosmological N-body simulations designed to provide a quantitative and accessible model of the evolution of the large-scale Universe. Such models are essential for many aspects of the study…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
We present a detailed comparison between numerical cosmological hydrodynamic zoom simulations and semi-analytic models (SAMs) run within merger trees extracted from the simulations. The high-resolution simulations represent 48 individual…
Preliminary results are presented from the CLEF hydrodynamics simulation, a large (N=2(428)^3 particles within a 200 Mpc/h comoving box) simulation of the LCDM cosmology that includes both radiative cooling and a simple model for galactic…
In this work, we present results for the photometric and clustering properties of galaxies that arise in a LambdaCDM hydrodynamical simulation of the local universe. The present-day distribution of matter was constructed to match the…