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-citationNearest neighbour distributions: New statistical measures for cosmological clustering
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Lee, J., Shin, J., Snaith, O. N., Kim, Y., Few, C. G., Devriendt, J., Dubois, Y., Cox, L. M., Hong, S. E., Kwon, O.-K., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abd08b · Cited by 85 · see citing works
Perna, M., Lanzuisi, G., Brusa, M., Mignoli, M., & Cresci, G. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201630369 · Cited by 84 · see citing works
Nuisance hardened data compression for fast likelihood-free inference
Alsing, J., & Wandelt, B. D. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1900 · Cited by 80 · see citing works
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 · see citing works
Self-citationDistribution and Evolution of Metals in the Magneticum Simulations
Dolag, K., Mevius, E., & Remus, R. (2017) · Galaxies · doi:10.3390/galaxies5030035 · Cited by 70 · see citing works
Super-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 · see citing works
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 · see citing works
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 · see citing works
Self-citationBishop, C. (1997) · Journal of the Brazilian Computer Society · doi:10.1590/s0104-65001997000200006 · Cited by 64 · cited 1× · see citing works
“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).”
BackgroundPrimordial 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 59 · see citing works
Self-citationThe 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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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 · see citing works
Large-scale dark matter simulations
Angulo, R., & Hahn, O. (2021) · Living Reviews in Computational Astrophysics · doi:10.1007/s41115-021-00013-z · Cited by 119 · see citing works
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 · see citing works
Botifoll, M., Pinto-Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 81 · see citing works
Towards an optimal estimation of cosmological parameters with the wavelet scattering transform
Valogiannis, G., & Dvorkin, C. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.103534 · Cited by 116 · see citing works
“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.”
BackgroundThe Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
“Answering this question is one of the motivations for the Cosmology and Astrophysics with Machine-learning Simulations (CAMELS) suite (Villaescusa-Navarro et al., 2021b), a set of about 4,000 simulations of 25(h−1Mpc)3 volumes which break down into 2, 000 dark matter only simulations, 1, 000…”
BackgroundMethodInfluentialFLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning
Kugel, R., Schaye, J., Schaller, M., Helly, J. C., Braspenning, J., Elbers, W., Frenk, C. S., McCarthy, I. G., Kwan, J., Salcido, J., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2540 · Cited by 96 · see citing works
Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids
Bayer, A. E., Villaescusa-Navarro, F., Massara, E., Liu, J., Spergel, D. N., Verde, L., Wandelt, B. D., Viel, M., & Ho, S. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac0e91 · Cited by 84 · see citing works
Self-citationThe MillenniumTNG Project: high-precision predictions for matter clustering and halo statistics
Hernández‐Aguayo, C., Springel, V., Pakmor, R., Barrera, M., Ferlito, F., White, S. D. M., Hernquist, L., Hadzhiyska, B., Delgado, A. M., Kannan, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1657 · Cited by 82 · see citing works
Self-citationStellar streams in the Gaia era
Bonaca, A., & Price-Whelan, A. M. (2024) · New Astronomy Reviews · doi:10.1016/j.newar.2024.101713 · Cited by 87 · see citing works
Applications and Techniques for Fast Machine Learning in Science
Deiana, A. M., Tran, N. V., Agar, J., Blott, M., Guglielmo, G. D., Duarte, J. M., Harris, P. C., Hauck, S., Liu, M., Neubauer, M. S., et al. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.787421 · Cited by 81 · see citing works
The CAMELS Project: Public Data Release
Villaescusa-Navarro, F., Genel, S., Anglés‐Alcázar, D., Perez, L. A., Villanueva-Domingo, P., Wadekar, D. S., Shao, H., Mohammad, F. G., Hassan, S., Moser, E., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/acbf47 · Cited by 73 · see citing works
“There are many possible machine-learning applications of this data set, e.g., (1) parameter inference (Villaescusa-Navarro et al. 2021a, 2021c), (2) summary or field level emulation, (3) mapping N-body to hydrodynamic simulations, (4) superresolution, and (5) time evolution.”
MethodInfluentialSelf-citationDark energy survey year 3 results: Cosmology with peaks using an emulator approach
Zürcher, D., Fluri, J., Sgier, R., Kacprzak, T., Gatti, M., Doux, C., Whiteway, L., Refregier, A. R., Chang, C., Jeffrey, N., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac078
Villaescusa-Navarro, F., Genel, S., Anglés-Alcázar, D., Thiele, L., Davé, R., Narayanan, D., Nicola, A., Li, Y., Villanueva-Domingo, P., Wandelt, B. D., et al. (2021) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac5ab0 · Cited by 69 · see citing works
“The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project (Villaescusa-Navarro et al. 2021a) has pioneered a new approach, producing thousands of cosmological hydrodynamic simulations to train machinelearning algorithms, varying cosmology, initial random field, subgrid…”
BackgroundMethodInfluentialSelf-citationNi, Y., Genel, S., Anglés‐Alcázar, D., Villaescusa-Navarro, F., Jo, Y., Bird, S., Matteo, T. D., Croft, R. A. C., Chen, N., de Santi, N. S. M., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ad022a · Cited by 65 · see citing works
“We use the model described in Villaescusa-Navarro et al. ( 2021c ) , which was trained on total matter maps from CAMELS-TNG simulations, to conduct tests on 15 distinct maps with constant values of Ω m and σ 8 ( shown with black horizontal lines ) from three different simulation suites: TNG ( green…”
BackgroundMethodInfluentialSelf-citationLange, J. U., Hearin, A. P., Leauthaud, A., van den Bosch, F. C., Guo, H., & DeRose, J. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3111 · Cited by 64 · see citing works
The baryon cycle in modern cosmological hydrodynamical simulations
Wright, R. J., Somerville, R. S., Lagos, C. D. P., Schaller, M., Davé, R., Anglés‐Alcázar, D., & Genel, S. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1688 · Cited by 63 · see citing works
Self-citationLearning Cosmology and Clustering with Cosmic Graphs
Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac8930 · Cited by 62 · see citing works
“In Villaescusa-Navarro et al. (2021c), the authors were able to infer the value of Ωm with an accuracy between 3.4% and 4.5% when using 2D total mass maps.”
MethodInfluentialSelf-citationGoing beyond the galaxy power spectrum: An analysis of BOSS data with wavelet scattering transforms
Valogiannis, G., & Dvorkin, C. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.103509 · Cited by 57 · see citing works
Pandey, S., Gatti, M., Baxter, E. J., Hill, J. C., Fang, X., Doux, C., Giannini, G., Raveri, M., DeRose, J., Huang, H.-J., et al. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.123526 · Cited by 47 · see citing works
Self-citationRobust Field-level Likelihood-free Inference with Galaxies
de Santi, N. S. M., Shao, H., Villaescusa-Navarro, F., Abramo, L. R., Teyssier, R., Villanueva-Domingo, P., Ni, Y., Anglés‐Alcázar, D., Genel, S., Hernández-Martínez, E., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acd1e2 · Cited by 46 · see citing works
“Perhaps the most challenging task associated with ML methods is their robustness (Hassani & Javanmard 2022), a hard question already explored using 2D maps with CNNs (Villaescusa-Navarro et al. 2021a), tabular data (Villaescusa-Navarro et al. 2022a), and galaxy catalogs (Villanueva-Domingo & Villaescusa-Navarro 2022).”
BackgroundMethodResultInfluentialSelf-citationVillaescusa-Navarro, F., Ding, J., Genel, S., Tonnesen, S. K., Torre, V. L., Spergel, D. N., Teyssier, R., Li, Y., Heneka, C. S., Lemos, P., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac5d3f · Cited by 44 · see citing works
“We refer the reader to Villaescusa-Navarro et al. (2021b) for further details on the simulations of the CAMELS project.”
BackgroundMethodInfluentialSelf-citationMangrove: Learning Galaxy Properties from Merger Trees
Jespersen, C., Cranmer, M., Melchior, P., Ho, S., Somerville, R., & Gabrielpillai, A. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/ac9b18 · Cited by 31 · see citing works
Self-citationThe bacco simulation project: bacco hybrid Lagrangian bias expansion model in redshift space
Pellejero-Ibáñez, M., Angulo, R. E., Zennaro, M., Stücker, J., Contreras, S., Aricò, G., & Maion, F. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad368 · Cited by 39 · see citing works
Machine learning for observational cosmology
Moriwaki, K., Nishimichi, T., & Yoshida, N. (2023) · Reports on Progress in Physics · doi:10.1088/1361-6633/acd2ea · Cited by 37 · see citing works
Ni, Y.-Y., Li, Y., Lachance, P., Croft, R., di Matteo, T., Bird, S., & Feng, Y. (2021) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2113 · Cited by 24 · see citing works
Self-citationCosmoGridV1: a simulated 𝗐CDM theory prediction for map-level cosmological inference
Kacprzak, T., Fluri, J., Schneider, A., Réfrégier, A., & Stadel, J. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/02/050 · Cited by 33 · see citing works
“In the order of increasing realism and computational cost, they can be categorized into (i) halo model-based baryonification schemes [86–88], (ii) subgrid models of stellar and Active Galactic Nucleus (AGN) [89, 90], or (iii) high resolution physical models relevant for galaxy formation, including AGN and black holes…”
BackgroundMethodBallard, D., Enzi, W., Collett, T. E., Turner, H. C., & Smith, R. J. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae514 · Cited by 36 · see citing works
Seppi, R., Comparat, J., Nandra, K., Dolag, K., Biffi, V., Bülbül, E., Liu, A., Ghirardini, V., & Chitham, J. I. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245138 · Cited by 36 · see citing works
Valogiannis, G., Yuan, S., & Dvorkin, C. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.103503 · Cited by 35 · see citing works
Breaking baryon-cosmology degeneracy with the electron density power spectrum
Nicola, A., Villaescusa-Navarro, F., Spergel, D. N., Dunkley, J., Anglés‐Alcázar, D., Davé, R., Genel, S., Hernquist, L., Nagai, D., Somerville, R. S., et al. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2022/04/046 · Cited by 35 · see citing works
Self-citationFinding Universal Relations in Subhalo Properties with Artificial Intelligence
Shao, H., Villaescusa-Navarro, F., Genel, S., Spergel, D. N., Anglés‐Alcázar, D., Hernquist, L., Davé, R., Narayanan, D., Contardo, G., & Vogelsberger, M. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac4d30 · Cited by 34 · see citing works
“…galaxy formation and evolution is not only important for galactic physics, but will also benefit other fields such as cosmology, where the uncertainty in galactic physics represents a major obstacle in extracting fundamental physics information from cosmic surveys (Villaescusa-Navarro et al. 2021).”
BackgroundMethodSelf-citationNeural Networks as Optimal Estimators to Marginalize Over Baryonic Effects
Villaescusa-Navarro, F., Wandelt, B. D., Anglés‐Alcázar, D., Genel, S., Matilla, J. M. Z., Ho, S., & Spergel, D. N. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac54a5 · Cited by 34 · see citing works
“This paper justifies the approach followed recently by the CAMELS project (Villaescusa-Navarro et al. 2021c).”
MethodSelf-citationPandey, S., Lehman, K., Baxter, E. J., Ni, Y., Anglés‐Alcázar, D., Genel, S., Villaescusa-Navarro, F., Delgado, A. M., & Matteo, T. D. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2268 · Cited by 34 · see citing works
Self-citationVogelsberger, M., Moser, E. B., Battaglia, N., Nagai, D., Lau, E. T., Valle, L. F. M. P., Villaescusa-Navarro, F., Amodeo, S., Anglés‐Alcázar, D., Bryan, G. L., et al. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000558174 · Cited by 34 · see citing works
Self-citationPredicting the impact of feedback on matter clustering with machine learning in CAMELS
Delgado, A. M., Anglés‐Alcázar, D., Thiele, L., Pandey, S., Lehman, K., Somerville, R. S., Ntampaka, M., Genel, S., Villaescusa-Navarro, F., & Hernquist, L. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2992 · Cited by 33 · see citing works
Self-citationProbing the Circumgalactic Medium with Fast Radio Bursts: Insights from CAMELS
Medlock, I., Nagai, D., Singh, P., Oppenheimer, B. D., Anglés‐Alcázar, D., & Villaescusa-Navarro, F. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad3070 · Cited by 32 · see citing works
Self-citationBretonnière, H., Boucaud, A., Lanusse, F., Jullo, E., Merlin, E., Tuccillo, D., Castellano, M., Brinchmann, J., Conselice, C. J., Dole, H. A., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141393 · Cited by 32 · see citing works
The flamingo project: baryon effects on the matter power spectrum
Schaller, M., Schaye, J., Kugel, R., Broxterman, J. C., & van Daalen, M. P. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf569 · Cited by 31 · see citing works
Wadekar, D., Thiele, L., Villaescusa-Navarro, F., Hill, J., Cranmer, M., Spergel, D., Battaglia, N., Anglés-Alcázar, D., Hernquist, L., & Ho, S. (2022) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2202074120 · Cited by 20 · see citing works
“It is worth mentioning that SR has been used in various astrophysical applications: modeling assembly bias (43, 44); estimating photometric redshifts of galaxies (57); inferring universal subhalo properties (47); modeling the concentration of dark matter from the mass distribution of nearby cosmic structures (39); dis…”
Self-citationWadekar, D. S., Thiele, L., Hill, J. C., Pandey, S., Villaescusa-Navarro, F., Spergel, D. N., Cranmer, M., Nagai, D., Anglés‐Alcázar, D., Ho, S., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1128 · Cited by 31 · see citing works
“We primarily use the CAMELS suite of simulations2 (Villaescusa-Navarro et al. 2021) which consists of 2,184 hydrodynamic simulations (each simulation box has side-length 25 h−1 Mpc).”
BackgroundMethodInfluentialSelf-citationDetermining the baryon impact on the matter power spectrum with galaxy clusters
Grandis, S., Aricò, G., Schneider, A., & Linke, L. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae259 · Cited by 31 · see citing works
The scatter in the galaxy–halo connection: a machine learning analysis
Stiskalek, R., Bartlett, D. J., Desmond, H., & Anbajagane, D. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1609 · Cited by 30 · see citing works
Cosmological baryon spread and impact on matter clustering in CAMELS
Gebhardt, M., Anglés‐Alcázar, D., Borrow, J., Genel, S., Villaescusa-Navarro, F., Ni, Y., Lovell, C. C., Nagai, D., Davé, R., Marinacci, F., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae817 · Cited by 29 · see citing works
Self-citationRobust simulation-based inference in cosmology with Bayesian neural networks
Lemos, P., Cranmer, M., Abidi, M. M., Hahn, C., Eickenberg, M., Massara, E., Yallup, D. P., & Ho, S. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acbb53 · Cited by 29 · see citing works
Self-citationHassan, S., Villaescusa-Navarro, F., Wandelt, B. D., Spergel, D. N., Anglés‐Alcázar, D., Genel, S., Cranmer, M., Bryan, G. L., Davé, R., Somerville, R. S., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac8b09 · Cited by 26 · see citing works
“We use simulations from the CAMELS project, that have been recently introduced in Villaescusa-Navarro et al. (2021c).”
BackgroundMethodInfluentialSelf-citationRobust Field-level Inference of Cosmological Parameters with Dark Matter Halos
Shao, H., Villaescusa-Navarro, F., Villanueva-Domingo, P., Teyssier, R., Garrison, L. H., Gatti, M., Inman, D., Ni, Y., Steinwandel, U. P., Kulkarni, M., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acac7a · Cited by 26 · see citing works
“In these simulations, that are part of the CAMELS project (Villaescusa-Navarro et al. 2021c), we vary the value of Ω m , σ 8 , the initial random seed, and four astrophysical parameters that control the efficiency of supernova and AGN feedback.”
BackgroundMethodInfluentialSelf-citationTillman, M., Burkhart, B., Tonnesen, S. K., Bird, S., Bryan, G. L., Anglés‐Alcázar, D., Hassan, S., Somerville, R. S., Davé, R., Marinacci, F., et al. (2023) · The Astronomical Journal · doi:10.3847/1538-3881/ad02f5 · Cited by 26 · see citing works
“The CAMELS simulations provide an excellent opportunity for this type of analysis (Villaescusa-Navarro et al. 2021).”
MethodInfluentialSelf-citationIyer, K. G., Speagle, J. S., Čaplar, N., Forbes, J. C., Gawiser, E., Leja, J., & Tacchella, S. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/acff64 · Cited by 26 · see citing works
Bernardini, M., Feldmann, R., Anglés-Alcázar, D., Boylan-Kolchin, M., Bullock, J., Mayer, L., & Stadel, J. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3088 · Cited by 26 · see citing works
Self-citationRodríguez, J., Rodríguez‐Rodríguez, I., & Woo, W. L. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1476 · Cited by 27 · see citing works
Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Perez, L. A., Genel, S., Villaescusa-Navarro, F., Somerville, R. S., Gabrielpillai, A., Anglés‐Alcázar, D., Wandelt, B. D., & Yung, L. Y. A. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/accd52 · Cited by 25 · see citing works
“TheCosmology and Astrophysics with MachinE Learning Simulations ( CAMELS ) project 1 (Villaescusa-Navarro et al. 2021a) posits: to constrain cosmology, we should leverage tools that can marginalize over uncertainties in bary-onic physics, and thereby measure the underlying cosmology.”
InfluentialSelf-citationSignatures of a parity-violating universe
Coulton, W. R., Philcox, O. H. E., & Villaescusa-Navarro, F. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.023531 · Cited by 25 · see citing works
Self-citationDSPS: Differentiable stellar population synthesis
Hearin, A. P., Chaves-Montero, J., Alarcon, A., Becker, M. R., & Benson, A. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad456 · Cited by 24 · see citing works
Predicting sub-millimetre flux densities from global galaxy properties
Cochrane, R. K., Hayward, C. C., Anglés‐Alcázar, D., & Somerville, R. S. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3451 · Cited by 23 · see citing works
Self-citationEfficient Long-range Active Galactic Nuclei (AGNs) Feedback Affects the Low-redshift Lyα Forest
Tillman, M., Burkhart, B., Tonnesen, S. K., Bird, S., Bryan, G. L., Anglés‐Alcázar, D., Davé, R., & Genel, S. (2023) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/acb7f1 · Cited by 23 · see citing works
“For this study, we explore small box TNG runs from the CAMELS project (Villaescusa-Navarro et al. 2021).”
BackgroundMethodInfluentialSelf-citationIntroducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations
Rose, J. C., Torrey, P., Villaescusa-Navarro, F., Lisanti, M., Nguyên, T., Roy, S., Kollmann, K. E., Vogelsberger, M., Cyr-Racine, F.-Y., Medvedev, M. V., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adb8e5 · Cited by 22 · see citing works
“The CAMELS project has tried to use machine learning to marginalize over numerical uncertainty from the galaxy formation implementation when predicting cosmological parameters (Villaescusa-Navarro et al. 2021a).”
InfluentialSelf-citationThe DESI PRObabilistic Value-added Bright Galaxy Survey (PROVABGS) Mock Challenge
Hahn, C., Kwon, K. J., Tojeiro, R., Siudek, M., Canning, R., Mezcua, M., Tinker, J. L., Brooks, D., Doel, P., Fanning, K., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ac8983 · Cited by 22 · see citing works
“Though such comparisons are currently limited by the computational costs of simulations, advances in machine-learning techniques for accelerating and emulating simulations will enable such comparisons to explore a broad range of galaxy formation models (e.g., Villaescusa-Navarro et al. 2021).”
BackgroundJo, Y., Genel, S., Wandelt, B. D., Somerville, R. S., Villaescusa-Navarro, F., Bryan, G. L., Anglés‐Alcázar, D., Foreman-Mackey, D. T., Nelson, D., & Kim, J.-H. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/aca8fe · Cited by 21 · see citing works
“…can be hardly constrained but also the AGN physics themselves has a negligible impact on the SFRD and the SMFs in the TNG suite of the CAMELS simulations that have low resolution and limited volume (refer to Section 6.1.1 for the SMFs and Figure 9 of Villaescusa-Navarro et al. (2021)) for the SFRD.”
BackgroundMethodInfluentialSelf-citationHadzhiyska, B., Ferraro, S., Pakmor, R., Bose, S., Delgado, A. M., Hernández‐Aguayo, C., Kannan, R., Springel, V., White, S. D. M., & Hernquist, L. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2751 · Cited by 21 · see citing works
Self-citationLeaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees
Chuang, C.-Y., Jespersen, C. K., Lin, Y., Ho, S., & Genel, S. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad2b6c · Cited by 21 · see citing works
“…models (SAMs; Kauffmann et al. 1993; Somerville et al. 2008; Croton et al. 2016) or hydrody-namical simulations (Pillepich et al. 2018; Villaescusa-Navarro et al. 2021; Pakmor et al. 2022) are required, at the cost of several tens of million CPU (central processing unit) hours, which…”
BackgroundSelf-citationElbers, W., Frenk, C. S., Jenkins, A. R., Li, B., Helly, J. C., Kugel, R., Schaller, M., Schaye, J., Braspenning, J., Kwan, J., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf093 · Cited by 21 · see citing works
李, W. F. 伟. L., 张, X. Y. 晓. Z., 牛, Y. F. 一. N., & Niu, Z.-M. (2023) · Journal of Physics G Nuclear and Particle Physics · doi:10.1088/1361-6471/ad0314 · Cited by 20 · see citing works
Taming assembly bias for primordial non-Gaussianity
Fondi, E., Verde, L., Villaescusa-Navarro, F., Baldi, M., Coulton, W. R., Jung, G., Karagiannis, D., Liguori, M., Ravenni, A., & Wandelt, B. D. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2024/02/048 · Cited by 20 · see citing works
Self-citationInferring galaxy dark halo properties from visible matter with machine learning
von Marttens, R., Casarini, L., Napolitano, N. R., Wu, S., Amaro, V., Li, R., Tortora, C., Canabarro, A., & Wang, Y. O. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2449 · Cited by 19 · see citing works
Effects of baryonic feedback on the cosmic web
Sunseri, J., Li, Z., & Liu, J. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.023514 · Cited by 19 · see citing works
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Piras, D., Joachimi, B., & Villaescusa-Navarro, F. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad052 · Cited by 19 · see citing works
Self-citationFerragamo, A., de Andrés, D., Sbriglio, A., Cui, W., Petris, M. D., Yepes, G., Dupuis, R., Jarraya, M., Lahouli, I., Luca, F. D., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad377 · Cited by 19 · see citing works
Cosmology with One Galaxy? The ASTRID Model and Robustness
Echeverri-Rojas, N., Villaescusa-Navarro, F., Chawak, C., Ni, Y., Hahn, C., Hernández-Martínez, E., Teyssier, R., Anglés‐Alcázar, D., Dolag, K., & Castro, T. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ace96e · Cited by 19 · see citing works
Self-citationPredictions for the ( n,2n ) reaction cross section based on a Bayesian neural network approach
Li, W. F., Liu, L., Niu, Z.-M., Niu, Y. F., & Huang, X. L. (2024) · Physical Review C · doi:10.1103/physrevc.109.044616 · Cited by 20 · see citing works
Shibuya, T., Ito, Y., Asai, K., Kirihara, T., Fujimoto, S., Toba, Y., Miura, N., Umayahara, T., IWADATE, K., Ali, S. S., et al. (2024) · Publications of the Astronomical Society of Japan · doi:10.1093/pasj/psae096 · Cited by 19 · see citing works
“…super-resolution techniques have been used in various domains of astronomy (e.g., Honma et al. 2014; Murata & Takeuchi 2022; Schawinski et al. 2017; Villaescusa-Navarro et al. 2021; Shirasaki et al. 2021; see also review papers of, e.g., Huertas-Company & Lanusse 2023 and Moriwaki et al. 2023),…”
Quantifying Baryonic Feedback on the Warm–Hot Circumgalactic Medium in CAMELS Simulations
Medlock, I., Neufeld, C., Nagai, D., Anglés‐Alcázar, D., Genel, S., Oppenheimer, B. D., Sims, X., Singh, P., & Villaescusa-Navarro, F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ada442 · Cited by 19 · see citing works
“For more information on the 1P28 set and the CAMELS project in general, we refer the reader to Villaescusa-Navarro et al. (2023, 2021); Ni et al. (2023).”
Self-citationConstraining Baryonic Feedback Effects on the Matter Power Spectrum with Fast Radio Bursts
Medlock, I., Nagai, D., Anglés‐Alcázar, D., & Gebhardt, M. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adbc9c · Cited by 18 · see citing works
“With this massive collection of simulations, the CAMELS project aims to enhance our understanding of baryonic feedback physics effects through the use of machine learning to marginalize over uncertainties in simulations (Villaescusa-Navarro et al. 2021, 2023).”
InfluentialSelf-citationSánchez, J., Omori, Y., Chang, C. L., Bleem, L. E., Crawford, T. M., Drlica-Wagner, A., Raghunathan, S., Zacharegkas, G., Abbott, T. M. C., Aguena, M., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1167 · Cited by 18 · see citing works
Balaguera-Antolínez, A., Kitaura, F.-S., Alam, S., Chuang, C.-H., Yu, Y., Favole, G., Sinigaglia, F., Zhao, C., Brooks, D., de la Macorra, A., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245618 · Cited by 18 · see citing works
Data compression and inference in cosmology with self-supervised machine learning
Akhmetzhanova, A., Mishra-Sharma, S., & Dvorkin, C. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3646 · Cited by 18 · see citing works
Roy, S., Shen, X., Barron, J., Lisanti, M., Curtin, D. Y., Murray, N., & Hopkins, P. F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adb02b · Cited by 17 · see citing works
“This could potentially be achieved with an approach similar to Villaescusa-Navarro et al. (2021); Brown et al. (2024); Rose et al. (2024).”
Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks
Perraudin, N., Marcon, S., Lucchi, A., & Kacprzak, T. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.673062 · Cited by 16 · see citing works
“Generative models have also been shown to work directly on the full or sliced 3D matter density distributions (Nathanaël et al., 2019; Tröster et al., 2019; Villaescusa-Navarro et al., 2020).”
BackgroundMethodMoser, E., Amodeo, S., Battaglia, N., Alvarez, M. A., Ferraro, S., & Schaan, E. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac0cea · Cited by 17 · see citing works
Self-citationPercent-level constraints on baryonic feedback with spectral distortion measurements
Thiele, L., Wadekar, D. S., Hill, J. C., Battaglia, N., Chluba, J., Villaescusa-Navarro, F., Hernquist, L., Vogelsberger, M., Anglés‐Alcázar, D., & Marinacci, F. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.083505 · Cited by 16 · see citing works
Self-citationZooming by in the CARPoolGP Lane: New CAMELS-TNG Simulations of Zoomed-in Massive Halos
Lee, M. E., Genel, S., Wandelt, B. D., Zhang, B., Delgado, A. M., Pandey, S., Lau, E. T., Carr, C., Cook, H. E., Nagai, D., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad3d4a · Cited by 16 · see citing works
“Scale cuts offers an advantage that the small, non-linear scales, shown to contain a wealth of cosmological parameter constraining power (Horowitz & Seljak 2017; Villaescusa-Navarro et al. 2021a,b; de Santi et al. 2023), can be incorporated into the analysis.”
BackgroundMethodInfluentialSelf-citationSorini, D., Bose, S., Pakmor, R., Hernquist, L., Springel, V., Hadzhiyska, B., Hernández‐Aguayo, C., & Kannan, R. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2613 · Cited by 16 · see citing works
Self-citationBayesian control variates for optimal covariance estimation with pairs of simulations and surrogates
Chartier, N., & Wandelt, B. D. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1837 · Cited by 15 · see citing works
Deciphering baryonic feedback with galaxy clusters
To, C., Pandey, S., Krause, E., Dalal, N., Anbajagane, D., & Weinberg, D. H. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2024/07/037 · Cited by 15 · see citing works
“Recent efforts have produced simulations spanning a wide range of free parameters regarding the physics of baryonic feedback from galaxy formation, with the aim of marginalizing over this uncertain galaxy formation physics in cosmological analyses [3–5].”
Lau, E. T., Nagai, D., Bogdán, Á., Medlock, I., Oppenheimer, B. D., Battaglia, N., Anglés‐Alcázar, D., Genel, S., Ni, Y., & Villaescusa-Navarro, F. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adc450 · Cited by 15 · see citing works
Self-citationGarcia, A. M., Torrey, P., Bhagwat, A., Wright, R. J., Chen, Q., Grasha, K., Ridolfo, S. G., Hemler, Z. S., Sarkar, A., Chakraborty, P., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adea51 · Cited by 15 · see citing works
Self-citationThe halo-finding problem revisited: a deep revision of the ASOHF code
Vallés-Pérez, D., Planelles, S., & Quilis, V. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243712
Modelling the galaxy–halo connection with semi-recurrent neural networks
Chittenden, H. G., & Tojeiro, R. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3498 · Cited by 14 · see citing works
Vallés-Pérez, D., Planelles, S., Monllor-Berbegal, Ó., & Quilis, V. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad059 · Cited by 14 · see citing works
Ono, V., Park, C. F., Mudur, N., Ni, Y., Cuesta-Lazaro, C., & Villaescusa-Navarro, F. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad5957 · Cited by 14 · see citing works
“In Figure 2 of Villaescusa-Navarro et al. (2021), the parameter inference network trained on input stellar fields was able to predict Ω m with an average error of 19 .”
BackgroundMethodInfluentialSelf-citationRobust neural network-enhanced estimation of local primordial non-Gaussianity
Giri, U., Münchmeyer, M., & Smith, K. M. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.l061301 · Cited by 14 · see citing works
Machine Learning and CosmologyPreprint
Dvorkin, C., Mishra-Sharma, S., Nord, B., Villar, V., Avestruz, C., Bechtol, K., Ciprijanovic, A., Connolly, A. J., Garrison, L., Narayan, G., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2203.08056 · Cited by 21 · see citing works
“…must be produced at large HPC facilities, using O (100K-1M) GPU-hours (e.g. AbacusSummit [77]; Uchuu [78]); or O (10M-100M) CPU-hours [79–82] (2) outputs are stored on large, high-throughput file systems, totalling O (1 PB); (3) the analysis (generation of data products) is executed in…”
BackgroundMethodSelf-citationLanzieri, D., Lanusse, F., Modi, C., Horowitz, B., Harnois-Déraps, J., & Starck, J. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346888 · Cited by 13 · see citing works
Estimating Galaxy Parameters with Self-organizing Maps and the Effect of Missing Data
Torre, V. L., Sajina, A., Goulding, A. D., Marchesini, D., Bezanson, R., Pearl, A., & Junior, L. S. (2024) · The Astronomical Journal · doi:10.3847/1538-3881/ad3821 · Cited by 13 · see citing works
“The Cosmology and Astrophysics with MachinE Learning Simulations ( CAMELS ) project ( Villaescusa-Navarro et al. 2021 ) was created to offer a large, state-of-the-art hydrodynamic simulation that can serve as a training data set for a variety of ML methods.”
BackgroundA field-level emulator for modelling baryonic effects across hydrodynamic simulations
Anonymous, Dai, B., Villaescusa-Navarro, F., & Seljak, U. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf355 · Cited by 13 · see citing works
Self-citationBaryonic imprints on DM haloes: the concentration–mass relation in the C amels simulations
Shao, M. (., Anbajagane, D., & Chang, C. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1620 · Cited by 13 · see citing works
Comparing weak lensing peak counts in baryonic correction models to hydrodynamical simulations
Lee, M. E., Lu, T., Haiman, Z., Liu, J., & Osato, K. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3592 · Cited by 12 · see citing works
High-fidelity reproduction of central galaxy joint distributions with neural networks
Rodrigues, N. V. N., de Santi, N. S. M., Montero-Dorta, A. D., & Abramo, L. R. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1186 · Cited by 12 · see citing works
Angeloudi, E., Falcón‐Barroso, J., Sarmiento, R., Pillepich, A., Walo-Martín, D., & Eisert, L. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1669 · Cited by 12 · see citing works
“Very few works have explored this issue until now, even though some momentum has been built recently with projects such as CAMELS (Villaescusa-Navarro et al. 2021), which has provided the community with a large dataset of galaxy simulations spanning a variety of standard cosmological parameters and…”
BackgroundSelf-citationNot hydro: using neural networks to estimate galaxy properties on a dark-matter-only simulation
Hernández, C. A., González, R., & Padilla, N. D. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2112 · Cited by 11 · see citing works
- 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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