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Found 3 papers, 1 papers with code
Robust field-level inference with dark matter halos
More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses.
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.
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
