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The EAGLE project: simulating the evolution and assembly of galaxies and their environments
Open accessAbstract
We introduce the Virgo Consortium's Evolution and Assembly of GaLaxies and their Environments (EAGLE) project, a suite of hydrodynamical simulations that follow the formation of galaxies and supermassive black holes in cosmologically representative volumes of a standard Λ cold dark matter universe. We discuss the limitations of such simulations in light of their finite resolution and poorly constrained subgrid physics, and how these affect their predictive power. One major improvement is our treatment of feedback from massive stars and active galactic nuclei (AGN) in which thermal energy is injected into the gas without the need to turn off cooling or decouple hydrodynamical forces, allowing winds to develop without predetermined speed or mass loading factors. Because the feedback efficiencies cannot be predicted from first principles, we calibrate them to the present-day galaxy stellar mass function and the amplitude of the galaxy-central black hole mass relation, also taking galaxy sizes into account. The observed galaxy stellar mass function is reproduced to ≲ 0.2 dex over the full resolved mass range, 108 < M*/M⊙ ≲ 1011, a level of agreement close to that attained by semi-analytic models, and unprecedented for hydrodynamical simulations. We compare our results to a representative set of low-redshift observables not considered in the calibration, and find good agreement with the observed galaxy specific star formation rates, passive fractions, Tully–Fisher relation, total stellar luminosities of galaxy clusters, and column density distributions of intergalactic C iv and O vi. While the mass–metallicity relations for gas and stars are consistent with observations for M* ≳ 109 M⊙ (M* ≳ 1010 M⊙ at intermediate resolution), they are insufficiently steep at lower masses. For the reference model, the gas fractions and temperatures are too high for clusters of galaxies, but for galaxy groups these discrepancies can be resolved by adopting a higher heating temperature in the subgrid prescription for AGN feedback. The EAGLE simulation suite, which also includes physics variations and higher resolution zoomed-in volumes described elsewhere, constitutes a valuable new resource for studies of galaxy formation.
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“The largest EAGLE cosmological simulation (Schaye et al. 2015), for example, contains 100 objects that are close analogs to the Milky Way in terms of stellar and dark mass, SFR, and bulge-to-disk ratio, of which a handful have dynamically similar stellar halos (see Bignone et al. 2019).”
From Streams, Substructures, and the Early History of the Milky Way (2020) · cites this paper as Schaye et al. 2015
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Citations
Cites
Galactic Stellar and Substellar Initial Mass Function
Chabrier, G. (2003) · Publications of the Astronomical Society of the Pacific · doi:10.1086/376392 · Cited by 9,071
The cosmological simulation code gadget-2
Springel, V. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09655.x · Cited by 6,368
Simulations of the formation, evolution and clustering of galaxies and quasars
Springel, V., White, S. D. M., Jenkins, A. R., Frenk, C. S., Yoshida, N., Gao, L., Navarro, J. F., Thacker, R. J., Croton, D., Helly, J. C., et al. (2005) · Nature · doi:10.1038/nature03597 · Cited by 5,060
Self-citationLewis, A., Challinor, A., & Lasenby, A. (2000) · The Astrophysical Journal · doi:10.1086/309179 · Cited by 4,920
The Global Schmidt law in star forming galaxies
Kennicutt, R., & Jr. (1997) · The Astrophysical Journal · doi:10.1086/305588 · Cited by 4,027
Galactic Stellar and Substellar Initial Mass FunctionPreprint
Chabrier, G. (2003) · Cited by 4,012
The cosmological simulation code GADGET-2
Petkova, M., & Springel, V. (2005) · Cited by 3,918
Croton, D. J., Springel, V., White, S. D. M., Lucia, G. D., Frenk, C. S., Gao, L., Jenkins, A., Kauffmann, G., Navarro, J. F., & Yoshida, N. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09675.x · Cited by 3,686
Self-citationTremonti, C. A., Heckman, T. M., Kauffmann, G., Brinchmann, J., Charlot, S., White, S. D. M., Seibert, M., Peng, E. W., Schlegel, D. J., Uomoto, A., et al. (2004) · The Astrophysical Journal · doi:10.1086/423264 · Cited by 3,608
Self-citationThe evolution of large-scale structure in a universe dominated by cold dark matter
Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. (1985) · The Astrophysical Journal · doi:10.1086/163168 · Cited by 3,397
Self-citation
Research works printed but not linked: 22
- Baldry, 2012, MNRAS, 421, 621
- Danforth, 2014, ApJ
- Davé, 2012, MNRAS, 421, 98
- Dutton, 2011, MNRAS, 416, 322
- Furlong, 2014, MNRAS
- Genel, 2014, MNRAS, 175
- Gerritsen, 1997, PhD thesis
- Hopkins, 2014, MNRAS, 581
- Horner, 2001, PhD thesis
- Khandai, 2014, MNRAS
- Kravtsov, 2014, ApJ
- Lemson, 2006, Virgo Consortium
- McCarthy, 2010, MNRAS, 406, 822
- Murante, 2010, MNRAS, 405, 1491
- Pontzen, 2008, MNRAS, 390, 1349
- Portinari, 1998, A&A, 334, 505
- Read, 2010, MNRAS, 405, 1513
- Rosas-Guevara, 2013, MNRAS
- Sawala, 2014, MNRAS
- Sawala, 2014, MNRAS
- Tully, 1977, A&A, 54, 661
- Vogelsberger, 2014, MNRAS, 1518
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“Largescale simulation efforts have enabled direct comparison to microwave observations (Hallman et al. 2007b; Schäfer et al. 2006; Kay et al. 2012; Schaye et al. 2015; Dolag et al. 2016; Spacek et al. 2018; Nelson et al. 2019).”
Background
Metadata
- Authors
- 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., Jenkins, A., Rosas-Guevara, Y. M., White, S. D. M., Baes, M., Booth, C. M., Camps, P., Navarro, J. F., Qu, Y., Rahmati, A., Sawala, T., Thomas, P. A., & Trayford, J.
- Affiliations
- Leiden University (NL)
- Durham University (GB)
- University of Antwerp (BE)
- Liverpool John Moores University (GB)
- Max Planck Society (DE)
- Ghent University (BE)
- University of Victoria (CA)
- University of Sussex (GB)
- Corresponding author
- Schaye, Joop
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-11
- Volume
- 446
- Issue
- 1
- Pages
- 521-554
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2119131940
- Cited by
- 3,601
- References
- 209
- Citations by year
- 2026: 225 · 2025: 326 · 2024: 340 · 2023: 338 · 2022: 369 · 2021: 338 · 2020: 362 · 2019: 356 · 2018: 285 · 2017: 293 · 2016: 226 · 2015: 137 · 2014: 6
- FWCI
- 116.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- Liverpool John Moores University (acceptedVersion)
- Durham Research Online (Durham University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Liverpool John Moores University (submittedVersion)
- Sussex Research Online (University of Sussex) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- Figshare (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (PHY11- 25915, ST/K003267/1, ST/H008519/1, FP7/2007-2013, ST/K00042X/1, NSF PHY-11-25915, 25915)
- European Commission (278594, NSF PHY11-25915, 2007–2013, 256573, FP7 2007-2013, FP7/2007)
- Durham University (ST/K00042X/1, ST/K003267/1, ST/H008519/1)
- Partnership for Advanced Computing in Europe AISBL
- Belgian Federal Science Policy Office (AP P7/08 CHARM)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (ST/K00042X/1, FP7 2007-2013, ST/H008519/1)
- Uniwersytet Warszawski
- Science and Technology Facilities Council (ST/I00162X/1, ST/H008519, ST/I004459/2, ST/K00042X/1, PERG06-GA-2009-256573, FP7/2007-2013, ST/L00075X/1, ST/L00061X/1, ST/F001166/1, ST/H008519/1, ST/I000976/1, ST/K003267/1, ST/L000652/1, ST/I004459/1)
- European Regional Development Fund (FP7/2007‐2013, 2007 – 2013)
- FP7 Ideas: European Research Council (267291, 321334)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysics and Star Formation Studies
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- EAGLE simulation, galaxy formation, galaxy assembly, supermassive black hole formation, AGN feedback, stellar feedback, fluid simulation, stellar mass function, galaxy size, specific star formation rate, quiescent galaxies, Tully-Fisher relation, galaxy clusters, intergalactic medium, mass-metallicity relation, galaxy groups, subgrid physics, resolution limit
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