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Found 6 papers, 5 papers with code
$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics.
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
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 stars from 2, 000 distinct simulated universes at several cosmic times.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
First results from SMAUG: Characterization of Multiphase Galactic Outflows from a Suite of Local Star-Forming Galactic Disk Simulations
We investigate the scaling of outflow mass, momentum, energy, and metal loading factors with galactic disk properties, including star formation rate (SFR) surface density (\Sigma_SFR~10^{-4}-1 M_sun/kpc^2/yr), gas surface density (~1-100 M_sun/pc^2), and total midplane pressure (or weight) (~10^3-10^6 k_B cm^{-3} K).
Astrophysics of Galaxies
A Cosmic Variance Cookbook
This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious.
Cosmology and Nongalactic Astrophysics
