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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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Self-citationLearning to predict the cosmological structure formation
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Self-citationThe diversity and variability of star formation histories in models of galaxy evolution
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Self-citationFisher for complements: extracting cosmology and neutrino mass from the counts-in-cells PDF
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Self-citationCosmological parameters and neutrino masses from the final Planck and full-shape BOSS data
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The impact of galactic feedback on the circumgalactic medium
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Self-citationUsing the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure
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Self-citationNew interpretable statistics for large-scale structure analysis and generation
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Self-citationThe dust-to-gas and dust-to-metal ratio in galaxies from z = 0 to 6
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Self-citationBlack hole – Galaxy correlations in simba
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Dolag, K., Mevius, E., & Remus, R. (2017) · Galaxies · doi:10.3390/galaxies5030035 · Cited by 73
Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA
Hassan, S., Andrianomena, S., & Doughty, C. C. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1151 · Cited by 70
Self-citationSuper-resolution emulator of cosmological simulations using deep physical models
Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1428 · Cited by 70
Self-citationPrimordial non-Gaussianity without tails – how to measure fNL with the bulk of the density PDF
Friedrich, O., Uhlemann, C., Villaescusa-Navarro, F., Baldauf, T., Manera, M., & Nishimichi, T. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2160 · Cited by 70
Self-citationThe DIANOGA simulations of galaxy clusters: characterising star formation in protoclusters
Bassini, L., Rasia, E., Borgani, S., Granato, G. L., Ragone-Figueroa, C., Biffi, V., Ragagnin, A., Dolag, K., Lin, W., Murante, G., et al. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038396 · Cited by 68
Barreira, A., Cabass, G., Schmidt, F., Pillepich, A., & Nelson, D. (2020) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2020/12/013 · Cited by 66
Self-citationBishop, C. (1997) · Journal of the Brazilian Computer Society · doi:10.1590/s0104-65001997000200006 · Cited by 64 · cited 1×
“In cases where this option is not available (e.g. simulations are too expensive), a different possibility is to estimate the errors using Bayesian neural networks (Blundell et al. 2015; Bishop 1997; Neal 1993; Mullachery et al. 2018).”
BackgroundThe impact of quenching on galaxy profiles in the simba simulation
Appleby, S., Davé, R., Kraljic, K., Anglés‐Alcázar, D., & Narayanan, D. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1169 · Cited by 57
Self-citationGalaxy power-spectrum responses and redshift-space super-sample effect
Li, Y., Schmittfull, M., & Seljak, U. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/02/022 · Cited by 54
Self-citationA Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys
Ntampaka, M., Eisenstein, D. J., Yuan, S., & Garrison, L. H. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab5f5e · Cited by 53
Matter trispectrum: theoretical modelling and comparison to N-body simulations
Gualdi, D., Novell-Masot, S., Gil-Marín, H., & Verde, L. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/01/015 · Cited by 51
Cosmological baryon transfer in the simba simulations
Borrow, J., Anglés‐Alcázar, D., & Davé, R. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3428 · Cited by 50
Self-citationInterpreting deep learning models for weak lensing
Matilla, J. M. Z., Sharma, M., Hsu, D., & Haiman, Z. (2020) · Physical Review D · doi:10.1103/physrevd.102.123506 · Cited by 48
Barreira, A., Nelson, D., Pillepich, A., Springel, V., Schmidt, F., Pakmor, R., Hernquist, L., & Vogelsberger, M. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1807 · Cited by 44
Self-citationJet feedback and the photon underproduction crisis in simba
Christiansen, J. F., Davé, R., Sorini, D., & Anglés‐Alcázar, D. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3007 · Cited by 40
Self-citationRemoving Astrophysics in 21 cm Maps with Neural Networks
Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/abd245 · Cited by 36
Self-citationStellar Dynamics and Star Formation Histories of z ∼ 1 Radio-loud Galaxies
Barišić, I., van der Wel, A., Bezanson, R., Pacifici, C., Noeske, K., Muñoz-Mateos, J. C., Franx, M., Smolčić, V., Bell, E. F., Brammer, G., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa8768 · Cited by 35
Teaching Neural Networks to Generate Fast Sunyaev–Zel’dovich Maps
Thiele, L., Villaescusa-Navarro, F., Spergel, D. N., Nelson, D., & Pillepich, A. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abb80f · Cited by 16
Self-citationOn the Reliability of Initial Conditions for Dissipationless Cosmological Simulations
Knebe, A., & Domínguez, Á. (2003) · Publications of the Astronomical Society of Australia · doi:10.1071/as02039 · Cited by 5
Cited by
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Schaye, J., Kugel, R., Schaller, M., Helly, J. C., Braspenning, J., Elbers, W., McCarthy, I. G., van Daalen, M. P., Vandenbroucke, B., Frenk, C. S., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2419 · Cited by 277
Large-scale dark matter simulations
Angulo, R. E., & Hahn, O. (2022) · Living Reviews in Computational Astrophysics · doi:10.1007/s41115-021-00013-z · Cited by 224
Hydrodynamical Simulations of the Galaxy Population: Enduring Successes and Outstanding Challenges
Crain, R. A., & van de Voort, F. (2023) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-041923-043618 · Cited by 138
Botifoll, M., Pinto‐Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 147
Towards an optimal estimation of cosmological parameters with the wavelet scattering transform
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“Last but not least, Convolutional Neural Networks (CNNs) [34] have recently shown great promise at improving cosmological parameter constraints by extracting information from non-Gaussian structures [35, 36], but fall prey to known interpretability issues associated with their application on real data.”
Background
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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