mlsanityData to 7 Oct 2026

The CAMELS project: public data release

arXiv 2201.01300 ↗First posted 4 Jan 2022astro-ph.CO astro-ph.GA astro-ph.IMcited by 6

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Abstract (arXiv)

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-α 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 urlhttps://camels.readthedocs.io.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 109 references that are in the map. First posted on arXiv 4 Jan 2022.

Builds on

11 of 109 references.

References · 11

  1. 2018Learning to Predict the Cosmological Structure Formationcited by 38
  2. 2021The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligencecited by 18
  3. 2020AI-assisted super-resolution cosmological simulationscited by 15
  4. 2021Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimationcited by 13
  5. 2021Inferring halo masses with Graph Neural Networkscited by 13
  6. 2018Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networkscited by 8
  7. 2020Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effectcited by 6
  8. 2020deep21: a Deep Learning Method for 21cm Foreground Removalcited by 6
  9. 2021AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statisticscited by 5
  10. 2020Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloescited by 2
  11. 2021Fast, high-fidelity Lyman α forests with convolutional neural networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 2.

6 papers.

  1. 2023Robust Field-level Likelihood-free Inference with Galaxiescited by 11
  2. 2025Multilevel neural simulation-based inferencecited by 5
  3. 2023Field-level simulation-based inference with galaxy catalogs: the impact of systematic effectscited by 4
  4. 2025Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Modelscited by 0
  5. 2026Conservative neural posterior estimation via distributionally robust trainingcited by 0
  6. 2026ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanetscited by 0