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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
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Superresolution emulation of large cosmological fields with a 3D conditional diffusion model
Rouhiainen, A., Münchmeyer, M., Shiu, G., Gira, M., & Lee, K. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.123536 · Cited by 22
“For example, invertible mappings were recently developed [42] between different cosmological fields in the CAMELS simulations [43].”
BackgroundComparison of models for the warm-hot circumgalactic medium around Milky Way-like galaxies
Singh, P., Lau, E. T., Faerman, Y., Stern, J. P., & Nagai, D. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1695 · Cited by 11
Xu, J., Eifler, T. F., Miranda, V., Fang, X., Saraivanov, E., Krause, E., Huang, H.-J., Benabed, K., & Zhong, K. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.063532 · Cited by 11
Towards unveiling the large-scale nature of gravity with the wavelet scattering transform
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Self-citationShankar, F., Bernardi, M., Roberts, D., Arana‐Catania, M., Grubenmann, T., Habouzit, M., Smith, A. B., Marsden, C., Varadarajan, K., Alonso-Tetilla, A. V., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf747 · Cited by 11
Self-citationLeung, C., Simha, S., Medlock, I., Nagai, D., Masui, K. W., Kahinga, L., Lanman, A. E., Andrew, S. E., Bandura, K., Curtin, A. P., et al. (2025) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ae044d · Cited by 11
An, G. C. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.901159 · Cited by 11
“, 2021) and cosmology (Villaescusa-Navarro et al., 2021)], but leads to off-target training if the simulation model is used to generate synthetic time series data.”
BackgroundCosmology with galaxy cluster properties using machine learning
Qiu, L., Napolitano, N. R., Borgani, S., Zhong, F., Li, X.-D., Radovich, M., Lin, W., Dolag, K., Tortora, C., Wang, Y. O., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346683 · Cited by 11
Emulating Sunyaev–Zeldovich images of galaxy clusters using autoencoders
Rothschild, T., Nagai, D., Aung, H., Green, S. B., Ntampaka, M., & ZuHone, J. A. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac438 · Cited by 12
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Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
de Santi, N. S. M., Villaescusa-Navarro, F., Abramo, L. R., Shao, H., Perez, L. A., Castro, T., Ni, Y., Lovell, C. C., Hernández-Martínez, E., Marinacci, F., et al. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/01/082 · Cited by 10
“…architecture of the GNNs (Section 3.2); (3) the use of the moment neural networks (MNN) to do the likelihood-free inference (Section 3.3); (4) the training procedure and optimization choices (Section 3.4); and (5) the evaluation of the models, where we present the metrics we analyze (Section 3.5).”
InfluentialSelf-citationQuantifying observational projection effects with a simulation-based hot CGM model
Shreeram, S., Comparat, J., Merloni, A., Zhang, Y., Ponti, G., Nandra, K., ZuHone, J. A., Marini, I., Vladutescu-Zopp, S., Popesso, P., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452271 · Cited by 10
Lovell, C. C., Starkenburg, T., Ho, M., Anglés‐Alcázar, D., Davé, R., Gabrielpillai, A., Iyer, K. G., Matthews, A. E., Roper, W. J., Somerville, R. S., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1888 · Cited by 24
Self-citationA Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Shao, H., de Santi, N. S. M., Villaescusa-Navarro, F., Teyssier, R., Ni, Y., Anglés‐Alcázar, D., Genel, S., Steinwandel, U. P., Hernández-Martínez, E., Dolag, K., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acee6f · Cited by 10
Self-citationInferring warm dark matter masses with deep learning
Rose, J. C., Torrey, P., Villaescusa-Navarro, F., Vogelsberger, M., O’Neil, S., Medvedev, M. V., Low, R., Adhikari, R., & Anglés‐Alcázar, D. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3260 · Cited by 10
Self-citationNanni, L., Neumann, J., Thomas, D., Maraston, C., Trayford, J. W., Lovell, C. C., Law, D. R., Yan, R., & Chen, Y. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3599 · Cited by 10
Baron, D., Netzer, H., Lutz, D., Davies, R., & Prochaska, J. X. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad39e9 · Cited by 9
Cosmology with Multiple Galaxies
Chawak, C., Villaescusa-Navarro, F., Echeverri-Rojas, N., Ni, Y., Hahn, C., & Anglés‐Alcázar, D. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad4969 · Cited by 9
“This is achieved by minimizing the following loss function: This loss function guarantees that µ, i and σ i represent the parameter’s posterior mean and standard deviation i (Jeffrey & Wandelt 2020; Villaescusa-Navarro et al. 2021b).”
BackgroundMethodInfluentialSelf-citationBullFrog: multi-step perturbation theory as a time integrator for cosmological simulations
Rampf, C., List, F., & Hahn, O. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/02/020 · Cited by 9
Inferring the equation of state from neutron star observables via machine learning
Patra, N. K., Malik, T., Pais, H., Zhou, K., Agrawal, B. K., & Providência, C. (2025) · Physics Letters B · doi:10.1016/j.physletb.2025.139470 · Cited by 9
“These applications exemplify the increasing collaboration between physics and machine learning and the great potential of the symbolic regression method to advance our understanding of the universe [51, 52].”
Vijayan, A., Krumholz, M. R., & Wibking, B. D. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf136 · Cited by 9
Weighing the Milky Way and Andromeda galaxies with artificial intelligence
Villanueva-Domingo, P., Villaescusa-Navarro, F., Genel, S., Anglés‐Alcázar, D., Hernquist, L., Marinacci, F., Spergel, D. N., Vogelsberger, M., & Narayanan, D. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.103003 · Cited by 26
Self-citationBusillo, V., Tortora, C., Napolitano, N. R., Koopmans, L. V. E., Covone, G., Gentile, F., & Hunt, L. K. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2691 · Cited by 9
Mercedes-Feliz, J., Anglés‐Alcázar, D., Oh, B. K., Hayward, C. C., Cochrane, R. K., Richings, A. J., Faucher‐Giguère, C., Wellons, S., Terrazas, B. A., Moreno, J., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1021 · Cited by 9
Self-citationCosmology from One Galaxy in a Void?
Wang, B. Y., & Pisani, A. (2024) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ad5ffe · Cited by 9
Perturbation-theory informed integrators for cosmological simulations
List, F., & Hahn, O. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.113201 · Cited by 9
“This suggests that – as long as Zel’dovich consistency is satisfied – the exact choice of the integrator now plays a smaller role than in the case of the larger simulation box in the Quijote experiment, as the non-linear scales in the Camels box are no longer well described by higher-order LPT corrections.”
MethodInfluentialLee, K. K., Coulton, W. R., Thiele, L., & Ho, S. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2602 · Cited by 9
Self-citationLokken, M., Cui, W., Bond, J. R., Hložek, R., Murray, N., Davé, R., & van Engelen, A. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1414 · Cited by 9
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Carvajal, R., Matute, I., Afonso, J., Norris, R. P., Luken, K., Sanchéz-Sáez, P., Cunha, P. A. C., Humphrey, A., Messias, H., Amarantidis, S., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245770 · Cited by 8
Brown, S. T., Fattahi, A., McCarthy, I. G., Font, A. S., Oman, K. A., & Riley, A. H. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1378 · Cited by 8
Clustering of binary black hole mergers: a detailed analysis of the eagle + mobse simulation
Peron, M., Ravenni, A., Libanore, S., Liguori, M., & Artale, M. C. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae893 · Cited by 8
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo
Mudur, N., Cuesta-Lázaro, C., & Finkbeiner, D. (2024) · Astrophysical Journal · doi:10.3847/1538-4357/ad8bc3 · Cited by 8
Quantifying the intrinsic variability due to randomness of the Auriga galaxy formation model
Pakmor, R., Bieri, R., Fragkoudi, F., Gómez, F. A., Grand, R. J. J., Simpson, C. M., Talbot, R. Y., van de Voort, F., & Werhahn, M. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1542 · Cited by 11
Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics
Zhou, A. J., Gatti, M., Anbajagane, D., Dodelson, S., Schaller, M., & Schaye, J. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/09/073 · Cited by 5
A rising tide: intrinsic alignments since the turn of the millennium
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Horowitz, B., & Lukić, Z. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1785 · Cited by 8
How Does Feedback Affect the Star Formation Histories of Galaxies?
Iyer, K. G., Starkenburg, T., Bryan, G. L., Somerville, R. S., Alfonzo, J. P., Anglés‐Alcázar, D., Cooray, S., Davé, R., Gabrielpillai, A., Genel, S., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae0334 · Cited by 8
“All runs have the same initial conditions and are evolved over a volume of (25 h − 1 Mpc) 3 , with further details found in Villaescusa-Navarro et al. (2021, 2023); Ni et al. (2023).”
InfluentialSelf-citationPerturbation theory remixed: Improved nonlinearity modeling beyond standard perturbation theory
Wang, Z., Jeong, D., Taruya, A., Nishimichi, T., & Osato, K. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.103534 · Cited by 8
Wang, Y., & He, P. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae229 · Cited by 8
Cosmological constraints from the density gradient weighted correlation function
Xiao, X., Yang, Y., Luo, X., Ding, J., Huang, Z., Wang, X., Zheng, Y., Sabiu, C. G., Forero-Romero, J. E., Miao, H., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac879 · Cited by 8
Correlations of Dark Matter, Gas, and Stellar Profiles in Dark Matter Halos
Farahi, A., Nagai, D., & Anbajagane, D. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac721e · Cited by 8
“Simulations such as the CAMELS project, a suite of simulations that systematically vary the subgrid physics and cosmological parameters, will enable such studies (Villaescusa-Navarro et al. 2021, 2022).”
MethodModern Cosmology, an Amuse-Gueule
Schmitz, K. (2022) · Springer eBooks · doi:10.1007/978-3-031-05625-3_3 · Cited by 10
Sanjaripour, S., Hemmati, S., Mobasher, B., Canalizo, G., Barish, B. C., Shivaei, I., Coil, A. L., Chartab, N., Jafariyazani, M., Reddy, N. A., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad90ba · Cited by 7
Constraining f N L using the large-scale modulation of small-scale statistics
Giri, U., Münchmeyer, M., & Smith, K. M. (2025) · Physical review. D/Physical review. D. · doi:10.1103/8h9r-rvhw · Cited by 7
Learning optimal summary statistics of galaxy catalogs with SBI
Lehman, K., Krippendorf, S., Weller, J., & Dolag, K. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/12/032 · Cited by 7
“In order to find machine-learned summary statistics and perform mock inference we make use of the CAMELS suite of simulations [19].”
Building Accurate Machine Learning Models for Predicting the Habitability of Exo-Planets
Raminaidu, C., Voosala, P., Swaroop, C. R., & Shankar, R. S. (2023) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit55814.2023.10061122 · Cited by 7
Improving convolutional neural networks for cosmological fields with random permutation
Zhong, K., Gatti, M., & Jain, B. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.043535 · Cited by 9
Hu, T., Khaire, V., Hennawi, J. F., Tripp, T. M., Oñorbe, J., Walther, M. R., & Lukić, Z. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2474 · Cited by 7
Simulation-based inference has its own Dodelson–Schneider effect (but it knows that it does)
Homer, J., Friedrich, O., & Gruen, D. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202453339 · Cited by 11
Discovering nuclear models from symbolic machine learning
Muñoz, J. M., Udrescu, S. M., & Ruíz, R. F. G. (2025) · Communications Physics · doi:10.1038/s42005-025-02023-2 · Cited by 7
Optimal machine-driven acquisition of future cosmological data
Kostić, A., Jasche, J., Ramanah, D. K., & Lavaux, G. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141706 · Cited by 7
Dome, T., Azhar, R., & Fialkov, A. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3897 · Cited by 7
de Andrés, D., Cui, W., Yepes, G., Petris, M. D., Ferragamo, A., Luca, F. D., Aversano, G., & Rennehan, D. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae071 · Cited by 7
Cosmology with fast-radio bursts
Glowacki, M., & Lee, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21439-4.00073-0 · Cited by 7
Accurate connected modeling of gas thermodynamics and matter distribution
Pandey, S., Salcido, J., To, C., Hill, J. C., Anbajagane, D., Baxter, E. J., & McCarthy, I. G. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.043529 · Cited by 6
Sunseri, J., Bayer, A. E., & Liu, J. (2025) · Physical review. D/Physical review. D. · doi:10.1103/grx3-hj7w · Cited by 6
Jo, Y., Genel, S., Sengupta, A. M., Wandelt, B. D., Somerville, R. S., & Villaescusa-Navarro, F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adec78 · Cited by 11
Self-citationSunseri, J., Amon, A., Dunkley, J., Battaglia, N., Ferraro, S., Hadzhiyska, B., Guachalla, B. R., & Schaan, E. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag031 · Cited by 6
Self-citationKuo, C.-T., Xu, D., & Friesen, R. (2025) · Universe · doi:10.3390/universe11120412 · Cited by 6
“For example, Villaescusa-Navarro et al. [202] use a fully unsupervised VAE on 2D gas map images and identify anomalies: images with significantly larger reconstruction errors compared to typical samples are classified as out-liers.”
MF-Box: multifidelity and multiscale emulation for the matter power spectrum
Ho, M.-F., Bird, S., Fernandez, M. A., & Shelton, C. R. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2901 · Cited by 6
slick: Modeling a Universe of Molecular Line Luminosities in Hydrodynamical Simulations
Garcia, K., Narayanan, D., Popping, G., Anirudh, R., Sutherland, S., & Kaasinen, M. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad642c · Cited by 6
Self-citationBhambra, P., Joachimi, B., Lahav, O., & Piras, D. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2810 · Cited by 6
Baryon Pasting the Uchuu Light-cone Simulation
Lau, E. T., Nagai, D., Farahi, A., Ishiyama, T., Miyatake, H., Osato, K., & Shirasaki, M. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ada940 · Cited by 6
“…to run, making them less suitable for exploring the wide parameter spaces required for upcoming large-scale multiwavelength surveys, where numerous combinations of cosmological and astrophysical parameters must be tested (Mead et al. 2021; Villaescusa-Navarro et al. 2021; Schaye et al. 2023).”
Cosmology from Point Clouds with Dark Matter Halos from the Quijote Simulations
Chatterjee, A., & Villaescusa-Navarro, F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adc99d · Cited by 6
“P. Villanueva-Domingo & F. Villaescusa-Navarro ( 2022 ) use a GNN-based approach to infer the value of cosmological parameters Ω m and σ 8 based on the galaxy catalogs from the CAMELS simulations ( F. Villaescusa-Navarro et al. 2021 ) .”
Self-citationInpainting Hydrodynamical Maps with Deep Learning
Mohammad, F. G., Villaescusa-Navarro, F., Genel, S., Anglés‐Alcázar, D., & Vogelsberger, M. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac9f14 · Cited by 6
“For this, we make use of data from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS; Villaescusa-Navarro et al. 2021) Multifield Data set (CMD; Villaescusa-Navarro et al. 2022b), a collection of hundreds of thousands of 2D maps and 3D grids containing 13 different fields…”
MethodSelf-citationEvaluating the feasibility of interpretable machine learning for globular cluster detection
Dold, D., & Fahrion, K. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243354 · Cited by 7
Predictive uncertainty on astrophysics recovery from multifield cosmology
Andrianomena, S., & Hassan, S. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/06/051 · Cited by 6
Self-citationChan, Y.-M., Manger, N., Li, Y., 楊, C.-C. 朝. Y., Zhu, Z., Armitage, P. J., & Ho, S. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ad088c · Cited by 6
“There are no obvious obstacles to training the network on a suite of simulations and thereby learning the dependence of particle clustering on physical input parameters ( see, e.g., analogous work in cosmology; Villaescusa-Navarro et al. 2021 ) .”
BackgroundMethodSelf-citationKvasiuk, Y., & Münchmeyer, M. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.083515 · Cited by 6
Cosmology with Galaxy Photometry Alone
Hahn, C., Villaescusa-Navarro, F., Melchior, P., & Teyssier, R. (2024) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ad3f1e · Cited by 6
“…used galaxies from 2,000 state-of-the-art hydrodynamical simulations with different cosmologies and astrophysical models from the CAMELS project (Villaescusa-Navarro et al. 2021, 2022a) to train moment networks (Jeffrey & Wandelt 2020) that predict cosmological parameters from galaxy properties.”
MethodSelf-citationsyren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Kammerer, L., Bartlett, D. J., Kronberger, G., Desmond, H., & Ferreira, P. G. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555887 · Cited by 5
Local primordial non-Gaussian bias from time evolution
Sullivan, J. M., & Seljak, U. (2025) · Physical review. D/Physical review. D. · doi:10.1103/8jvj-66zc · Cited by 5
“Figure 5 shows results similar to those presented in Figure 3 at z = 1, but for simulated galaxies from the CAMELS suite of hydrodynamical simulations [73] with the IllustrisTNG galaxy formation model [74].”
Zhang, Z., Nagamine, K., Oku, Y., Lee, K., Fukushima, K., Tomaru, K., Zhang, B., Medlock, I., & Nagai, D. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae00c2 · Cited by 5
Schwitzkowski, M., Veeranki, S. P. K., Seidel, B. N., Kindle, G. R., Rusch, S., Elaziz, D. A., Abdelkader, S. L. M., Abitbol, A., Abolhassani, H., Abrahim, L., et al. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.10.022 · Cited by 5
Multi-epoch machine learning 2: identifying physical drivers of galaxy properties in simulations
McGibbon, R. J., & Khochfar, S. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1811 · Cited by 5
“The CAMELS project4 (Villaescusa-Navarro et al. 2021, 2023) contains two different suites of state-of-the-art hydrodynamic simulations.”
BackgroundSecondary halo bias through cosmic time
Balaguera-Antolínez, A., & Montero-Dorta, A. D. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202451484 · Cited by 5
The cosmological analysis of X-ray cluster surveys
Kosiba, M., Cerardi, N., Pierre, M., Lanusse, F., Garrel, C., Werner, N., & Shalak, M. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202450499 · Cited by 5
Two-field formalism for a neural network-enhanced non-Gaussianity search with halos
Kvasiuk, Y., Münchmeyer, M., & Smith, K. M. (2025) · Physical review. D/Physical review. D. · doi:10.1103/2szy-wypg · Cited by 5
Nguyên, T., Villaescusa-Navarro, F., Mishra-Sharma, S., Cuesta-Lazaro, C., Torrey, P., Farahi, A., Garcia, A. M., Rose, J. C., O’Neil, S., Vogelsberger, M., et al. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae28dc · Cited by 26
“Similar to the CAMELS project ( F. Villaescusa-Navarro et al. 2021 ) , these simulations vary in both cosmological and astrophysical parameters.”
Self-citationNuclear charge radius predictions based on eXtreme Gradient Boosting
Li, W., Zhang, X., & Fang, J. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad3170 · Cited by 6
Supervised, semisupervised, and unsupervised learning of the Domany-Kinzel model
Tuo, K., Li, W., Deng, S., & Zhu, Y. (2024) · Physical review. E · doi:10.1103/physreve.110.024102 · Cited by 5
The Cosmological analysis of X-ray cluster surveys
Cerardi, N., Pierre, M., Lanusse, F., & Corap, X. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202453553 · Cited by 4
Modeling the Cosmological Lyman- α Forest at the Field Level
de Belsunce, R., Ivanov, M. M., Sullivan, J. M., Akitsu, K., & Chen, S.-F. (2026) · Physical Review Letters · doi:10.1103/8g9h-68ms · Cited by 4
“Our model can be used to bridge the Ly-α forest maps on large and small scales by analytically generating the large-scale models whose properties match the small-volume high-resolution simulations (e.g. Illustris TNG [106, 107], ACCEL 2 [108], CAMELS [109, 110], PRIYA [111]).”
Doeser, L., Ata, M., & Jasche, J. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1289 · Cited by 4
Hernández-Martínez, E., Genel, S., Villaescusa-Navarro, F., Steinwandel, U. P., Lee, M. E., Lau, E. T., & Spergel, D. N. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ada9e9 · Cited by 4
“CAMELS uses thousands of state-of-the-art hydrodynamic simulations to train models that can extract intricate patterns from large-scale structure data.”
InfluentialSelf-citationThe Hot Circumgalactic Medium in Stacked X-Rays: Observations versus Simulations
Grayson, S., Scannapieco, E. S., Comparat, J., ZuHone, J. A., Zhang, Y., Shreeram, S., Brüggen, M., & Bülbül, E. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae100f · Cited by 4
Cosmological Parameter Constraints from the SDSS Density and Momentum Power Spectra
Appleby, S. A., Tonegawa, M., Park, C., Hong, S. E., Kim, J., & Yoon, Y. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acff68 · Cited by 4
“Alternative approaches to the standard N -point functions are motonari.tonegawa@apctp.org also progressively being explored (Pisani et al. 2015; Pan et al. 2020; Uhlemann et al. 2020; Villaescusa-Navarro et al. 2021; Appleby et al. 2022; Qin et al. 2023).”
BackgroundCharacterizing structure formation through instance segmentation
López-Cano, D., Stücker, J., Pellejero-Ibáñez, M., Angulo, R. E., & Franco-Barranco, D. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348965 · Cited by 8
Can We Constrain Warm Dark Matter Masses with Individual Galaxies?
Lin, S., Villaescusa-Navarro, F., Rose, J. C., Torrey, P., Farahi, A., Kollmann, K. E., Garcia, A. M., Roy, S., Vogelsberger, M., Kallivayalil, N., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad54b6 · Cited by 4
Self-citationInvestigating the Kinematics of Central and Satellite Galaxies Using Normalizing Flows
Kwon, K. J., & Hahn, C. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad8442 · Cited by 4
Towards cosmological inference on unlabeled out-of-distribution HI observational data
Andrianomena, S., & Hassan, S. (2024) · Astrophysics and Space Science · doi:10.1007/s10509-025-04405-y · Cited by 4
“In this work we consider the multifield dataset [38] from the CAMELS Project [19, 39].”
Self-citationMapping circumgalactic medium observations to theory using machine learning
Appleby, S., Davé, R., Sorini, D., Lovell, C. C., & Lo, K. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2266 · Cited by 4
The impact of the WHIM on the IGM thermal state determined from the low-z Lyman α forest
Hu, T., Khaire, V., Hennawi, J. F., Oñorbe, J., Walther, M. R., Lukić, Z., & Davies, F. B. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3846 · Cited by 4
Constraining Neutrino Cosmologies with Nonlinear Reconstruction
Zang, S.-H., & Zhu, H.-M. (2023) · Astrophysical Journal · doi:10.3847/1538-4357/ad0f97 · Cited by 2
“5 h Mpc − 1 , where the bary-onic effects are subdominant (see e.g. Chisari et al. 2019; Villaescusa-Navarro et al. 2021).”
BackgroundShao, M., & Anbajagane, D. (2024) · The Open Journal of Astrophysics · doi:10.33232/001c.116969 · Cited by 4
Cosmological simulations of galaxies
Feldmann, R., & Bieri, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21439-4.00111-5 · Cited by 4
CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations
Busillo, V., Tortora, C., Covone, G., Koopmans, L. V. E., Silvestrini, M., & Napolitano, N. R. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202451702 · Cited by 3
Field-level Comparison and Robustness Analysis of Cosmological N -body Simulations
Bayer, A. E., Villaescusa-Navarro, F., Sharief, S. N., Teyssier, R., Garrison, L. H., Perreault-Levasseur, L., Bryan, G. L., Gatti, M., & Visbal, E. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adef4e · Cited by 3
“To perform inference , simulation-based inference (SBI) is an emerging tool to determine the cosmological parameters by training a neural network on cosmological simulations (see e.g. Villaescusa-Navarro et al. 2021a; Hahn et al. 2024; Ho et al. 2024; Tucci & Schmidt 2024).”
Self-citationHarvey, D., Revaz, Y., Schaller, M., Schneider, A., Tregidga, E., & Vecchi, F. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555180 · Cited by 6
Interpreting Cosmological Information from Neural Networks in the Hydrodynamic Universe
Lahiry, A., Bayer, A. E., & Villaescusa-Navarro, F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae0b5a · Cited by 3
“These maps are available in the CAMELS Multifield Dataset (CMD) (Villaescusa-Navarro et al. 2022); we refer the reader to Villaescusa-Navarro et al. (2021) and Villaescusa-Navarro et al. (2022) for further details on the simulations of the CAMELS project.”
InfluentialSelf-citation
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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