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The CAMELS Project: Cosmology and Astrophysics with Machine-learning Simulations
Open accessAbstract
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. CAMELS is a suite of 4233 cosmological simulations of 25 h − 1 Mpc 3 volume each: 2184 state-of-the-art (magneto)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto)hydrodynamic simulations designed to train machine-learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying Ω m , σ 8, and four parameters controlling stellar and active galactic nucleus feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of ( 400 h − 1 Mpc ) 3 . We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine-learning applications, including nonlinear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks, dimensionality reduction, and anomaly detection.
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Citations
Cites
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Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Planck 2018 results: VI. Cosmological parameters
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The cosmological simulation code gadget-2
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The Global Schmidt law in star forming galaxies
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MethodInfluentialThe EAGLE project: simulating the evolution and assembly of galaxies and their environments
Schaye, J., Crain, R. A., Bower, R. G., Furlong, M., Schaller, M., Theuns, T., Dalla Vecchia, C., Frenk, C. S., McCarthy, I. G., Helly, J. C., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2058 · Cited by 3,601
The evolution of large-scale structure in a universe dominated by cold dark matter
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Populating a cluster of galaxies - I. Results at \fontshape{it}{z}=0
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On Spherically Symmetrical Accretion
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“SMBH growth by gas accretion follows the spherical Bondi (1952) parameterization, limited by the Eddington rate.”
BackgroundMethodE pur si muove: Galilean-invariant cosmological hydrodynamical simulations on a moving mesh
Springel, V. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15715.x · Cited by 2,427 · cited 3×
“Simulations CAMELS is a suite of 4,233 numerical simulations, including both N-body simulations run with GadgetIII (Springel 2005) and state-of-the-art (magneto)hydrodynamic simulations run with AREPO (Springel 2010) and GIZMO (Hopkins 2015).”
MethodStatistical Properties of X‐Ray Clusters: Analytic and Numerical Comparisons
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Byeol-Haneul/CosmoTopo: v.1.0.1
Lee, J.-Y. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15642762
BOA constrictor: a Mamba-based lossless compressor for scientific data
Gupta, A., Djobava, T., & Elliott, T. J. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae64a9
“As case studies, we consider computational fluid dynamics (CFD; synthetic isotropic turbulence data, see appendix F) and Cosmology & Astrophysics (CAMELS IllustrisTNG [62–64]) datasets (905.97 and 661.10 MB respectively).”
Influential
Metadata
- Authors
- Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., Spergel, D. N., S. Somerville, R., Dave, R., Pillepich, A., Hernquist, L., Nelson, D., Torrey, P., Narayanan, D., Li, Y., Philcox, O., La Torre, V., Maria Delgado, A., Ho, S., Hassan, S., Burkhart, B., Wadekar, D., Battaglia, N., Contardo, G., & Bryan, G. L.
- Affiliations
- Princeton University (US)
- Flatiron Institute (US)
- Royal Observatory (GB)
- Max Planck Institute for Astronomy (DE)
- Center for Astrophysics Harvard & Smithsonian (US)
- Heidelberg University (DE)
- University of Florida (US)
- Rutgers, The State University of New Jersey (US)
- New York University (US)
- Cornell University (US)
- ORCID
- Venue
- The Astrophysical Journal
- Publisher
- American Astronomical Society
- Published
- 2021-07-01
- Volume
- 915
- Issue
- 1
- Pages
- 71
- ISSN
- 0004-637X (ISSN-L)
- 1538-4357
- OpenAlex
- W3090580769
- Cited by
- 304
- References
- 105
- Citations by year
- 2026: 52 · 2025: 80 · 2024: 61 · 2023: 60 · 2022: 42 · 2021: 9
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Astrophysical Journal (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Science and Technology Facilities Council (ST/R000972/1)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- cosmological hydrodynamical simulations, machine learning training data, stellar feedback, AGN feedback, matter power spectrum, galaxy stellar mass function, galaxy scaling relations, star formation rate density, cosmological parameter space, baryonic effects, nonlinear interpolation, parameter estimation, symbolic regression, generative adversarial networks, anomaly detection, dimensionality reduction, IllustrisTNG, SIMBA, AREPO, GIZMO
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