Hostname: page-component-7c8c6479df-94d59 Total loading time: 0 Render date: 2024-03-29T14:53:55.523Z Has data issue: false hasContentIssue false

Formation and evolution of globular clusters in cosmological simulations

Published online by Cambridge University Press:  11 March 2020

Hui Li
Affiliation:
Department of Physics, Kavli Institute for Astrophysics and Space Research, MIT, Cambridge, MA02139, USA email: hliastro@mit.edu
Oleg Gnedin
Affiliation:
Department of Astronomy, University of Michigan, Ann Arbor, MI48109, USA email: ognedin@umich.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In a series of three papers, we introduced a novel cluster formation model that describes the formation, growth, and disruption of star clusters in high-resolution cosmological simulations. We tested this model on a Milky Way-sized galaxy and found that various properties of young massive clusters, such as the mass function and formation efficiency, are consistent with observations in the local universe. Interestingly, most massive clusters – globular cluster candidates – are preferentially formed during major merger events. We follow the dynamical evolution of clusters in the galactic tidal field. Due to tidal disruption, the cluster mass function evolves from initial power law to a peaked shape. The surviving clusters at z = 0 show a broad range of metallicity [Fe/H] from -3 to -0.5. A robust prediction of the model is the age–metallicity relation, in which metal-rich clusters are systematically younger than metal-poor clusters by up to 3 Gyr.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

Footnotes

Hui Li is a Hubble fellow.

References

Adamo, A., Kruijssen, J. M. D., Bastian, N., Silva-Villa, E., & Ryon, J. 2015, MNRAS, 452, 246CrossRefGoogle Scholar
Ashman, K. M. & Zepf, S. E. 1992, ApJ, 384, 5010.1086/170850CrossRefGoogle Scholar
Brodie, J. P. & Strader, J. 2006, ARA&A, 44, 193CrossRefGoogle Scholar
Choksi, N., Gnedin, O. Y., & Li, H. 2018, MNRAS, 480, 2343CrossRefGoogle Scholar
Choksi, N. & Gnedin, O. Y. 2019, MNRAS, 486, 331CrossRefGoogle Scholar
El-Badry, K., Quataert, E., Weisz, D. R., Choksi, N., & Boylan-Kolchin, M. 2019, MNRAS, 482, 4528CrossRefGoogle Scholar
Kim, J.et al. 2018, MNRAS, 474, 4232CrossRefGoogle Scholar
Lahén, N., Naab, T., Johansson, P. H., Elmegreen, B., Hu, C.-Y., & Walch, S. 2019, ApJL, 879, L1810.3847/2041-8213/ab2a13CrossRefGoogle Scholar
Leaman, R., VandenBerg, D. A., & Mendel, J. T. 2013, MNRAS, 436, 122CrossRefGoogle Scholar
Li, H. & Gnedin, O. Y., 2014, ApJ, 796, 1010.1088/0004-637X/796/1/10CrossRefGoogle Scholar
Li, H., Gnedin, O. Y., Gnedin, N. Y., Meng, X., Semenov, V. A., & Kravtsov, A. V. 2017, ApJ, 834, 69CrossRefGoogle Scholar
Li, H., Gnedin, O. Y., & Gnedin, N. Y. 2018, ApJ, 861, 107CrossRefGoogle Scholar
Li, H. & Gnedin, O. Y. 2019, MNRAS, 486, 403010.1093/mnras/stz1114CrossRefGoogle Scholar
Li, H., Vogelsberger, M., Marinacci, F., & Gnedin, O. Y. 2019, MNRAS, 487, 36410.1093/mnras/stz1271CrossRefGoogle Scholar
Ma, X.et al. 2019, arXiv:1906.11261Google Scholar
Muratov, A. L. & Gnedin, O. Y. 2010, ApJ, 718, 126610.1088/0004-637X/718/2/1266CrossRefGoogle Scholar
Pfeffer, J., Kruijssen, J. M. D., Crain, R. A., & Bastian, N. 2018, MNRAS, 475, 4309CrossRefGoogle Scholar
Ramos-Almendares, F., Sales, L. V., Abadi, M. G., Doppel, J. E., Muriel, H., & Peng, E. W. 2019, arXiv:1906.11921Google Scholar