A 3D CNN encoder plus a masked autoregressive flow recovers Ωm and σ8 from simulated HI tomographic data cubes at z=1 with R² ≥ 0.91 on test sets, with reduced but still useful accuracy out of distribution.
The CAMELS project: public data release
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abstract
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. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$\alpha$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.
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Probabilistic cosmological inference on HI tomographic data
A 3D CNN encoder plus a masked autoregressive flow recovers Ωm and σ8 from simulated HI tomographic data cubes at z=1 with R² ≥ 0.91 on test sets, with reduced but still useful accuracy out of distribution.