The CAMELS project: public data release

• DOI: 10.48550/arxiv.2201.01300
• Publication Date: 2022-01-01
• Authors: Francisco Villaes...; Shy Genel; Daniel Anglés-Alc...; Lucia A. Perez; Pablo Villanueva-...; Digvijay Wadekar; Helen Shao; Faizan G Mohammad; Sultan Hassan; E. Barry Moser; Erwin T. Lau; Luis Fernando Mac...; Andrina Nicola; Leander Thiele; Yongseok Jo; Oliver H. E. Philcox; Benjamin D. Oppen...; Megan Tillman; ChangHoon Hahn; Neerav Kaushal; Alice Pisani; Matthew Gebhardt; Ana María Delgado; Joyce Caliendo; Christina D. Kreisch; Kaze W. K. Wong; William R. Coulton; Michael Eickenberg; Gabriele Parimbelli; Yueying Ni; Ulrich P. Steinwa...; Valentina La Torre; Romeel Davé; Nicholas Battaglia; Daisuke Nagai; David N. Spergel; Lars Hernquist; Blakesley Burkhart; Desika Narayanan; B. D. Wandelt; Rachel S. Somerville; Greg L. Bryan; Matteo Viel; Yin Li; Vid Iršič; Katarina Kraljic; Mark Vogelsberger
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Cosmology and Nongalactic Astrophysics (astro-ph.CO); Computer Science - Artificial Intelligence; Hydrodynamical simulations; FOS: Physical sciences; [INFO] Computer Science [cs]; Astrophysics; 01 natural sciences; Machine Learning (cs.LG); Astrostatistics; 0103 physical sciences; Cosmology; Hydrodynamical simulations; Astrostatistics; Galaxy formation; Instrumentation and Methods for Astrophysics (astro-ph.IM); Galaxy formation; Astrostatistics; Cosmology; Galaxy formation; Hydrodynamical simulations; Astrophysics - Astrophysics of Galaxies; Cosmology; QB460-466; Artificial Intelligence (cs.AI); [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]; Astrophysics of Galaxies (astro-ph.GA); [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]; Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology astrophysics through thousands of cosmological hydrodynamic simulations machine learning. CAMELS contains 4,233 simulations, 2,049 N-body 2,184 state-of-the-art that sample a vast volume in parameter space. In this paper we present the public data release, describing characteristics variety products generated from them, including halo, subhalo, galaxy, void catalogues, power spectra, bispectra, Lyman-$α$ probability distribution functions, halo radial profiles, X-rays photon lists. We also release over one thousand catalogues contain billions galaxies CAMELS-SAM: large collection have been combined Santa Cruz Semi-Analytic Model. all data, comprising more than 350 terabytes containing 143,922 snapshots, millions halos, summary statistics. provide further technical details on how access, download, read, process at \url{https://camels.readthedocs.io}.