Hostname: page-component-7bb8b95d7b-s9k8s Total loading time: 0 Render date: 2024-09-20T18:37:28.886Z Has data issue: false hasContentIssue false

Using Gaia for studying Milky Way star clusters

Published online by Cambridge University Press:  11 March 2020

Eugene Vasiliev*
Affiliation:
Institute of Astronomy, University of Cambridge, UK Lebedev Physical Institute, Moscow, Russia email: eugvas@lpi.ru
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.

We review the implications of the Gaia Data Release 2 catalogue for studying the dynamics of Milky Way globular clusters, focusing on two separate topics.

The first one is the analysis of the full 6-dimensional phase-space distribution of the entire population of Milky Way globular clusters: their mean proper motions (PM) can be measured with an exquisite precision (down to 0.05 mas yr−1, including systematic errors). Using these data, and a suitable ansatz for the steady-state distribution function (DF) of the cluster population, we then determine simultaneously the best-fit parameters of this DF and the total Milky Way potential. We also discuss possible correlated structures in the space of integrals of motion.

The second topic addresses the internal dynamics of a few dozen of the closest and richest globular clusters, again using the Gaia PM to measure the velocity dispersion and internal rotation, with a proper treatment of spatially correlated systematic errors. Clear rotation signatures are detected in 10 clusters, and a few more show weaker signatures at a level ∼0.05 mas yr−1. PM dispersion profiles can be reliably measured down to 0.1 mas yr−1, and agree well with the line-of-sight velocity dispersion profiles from the literature.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Baumgardt, H., Hilker, M., Sollima, A., & Bellini, A. 2019, MNRAS, 482, 5138CrossRefGoogle Scholar
Bianchini, P., van der Marel, R., del Pino, A., et al. 2018, MNRAS, 481, 2125CrossRefGoogle Scholar
Binney, J. & Wong, L. K. 2017, MNRAS, 467, 2446Google Scholar
Bovy, J. 2015, ApJS, 216, 2910.1088/0067-0049/216/2/29CrossRefGoogle Scholar
Eadie, G. & Jurić, M. 2019, ApJ, 875, 15910.3847/1538-4357/ab0f97CrossRefGoogle Scholar
Harris, W. 1996, AJ, 112, 1487; https://arxiv.org/abs/1012.3224 10.1086/118116CrossRefGoogle Scholar
Gaia Collaboration (Brown et al.), 2018a, A&A, 616, 1Google Scholar
Gaia Collaboration (Helmi et al.), 2018b, A&A, 616, 12Google Scholar
Helmi, A., Babusiaux, C., Koppelman, H., et al. 2018, Nature, 563, 8510.1038/s41586-018-0625-xCrossRefGoogle Scholar
Jindal, A., Webb, J., & Bovy, J. 2019, MNRAS, 487, 369310.1093/mnras/stz1586CrossRefGoogle Scholar
Law, D. & Majewski, S. 2010, ApJ, 718, 1128CrossRefGoogle Scholar
Lindegren, L., Hernandez, J., Bombrun, A., et al. (Gaia Collaboration), 2018, A&A, 616, 2Google Scholar
McMillan, P. 2017, MNRAS, 465, 76CrossRefGoogle Scholar
Myeong, G. C., Evans, N. W., Belokurov, V., Sanders, J., & Koposov, S. 2018, ApJL, 863, L28CrossRefGoogle Scholar
Myeong, G. C., Vasiliev, E., Iorio, G., Evans, N. W., & Belokurov, V. 2019, MNRAS, 488, 1235CrossRefGoogle Scholar
Posti, L. & Helmi, A. 2019, A&A, 621, 56Google Scholar
Sollima, A., Baumgardt, H., & Hilker, M. 2019, MNRAS, 485, 146010.1093/mnras/stz505CrossRefGoogle Scholar
Vasiliev, E. 2019a, MNRAS, 484, 283210.1093/mnras/stz171CrossRefGoogle Scholar
Vasiliev, E. 2019b, MNRAS, 489, 62310.1093/mnras/stz2100CrossRefGoogle Scholar
Watkins, L., van der Marel, R., Bellini, A., & Anderson, J. 2015, ApJ, 803, 2910.1088/0004-637X/803/1/29CrossRefGoogle Scholar
Watkins, L., van der Marel, R., Sohn, S.T., & Evans, N. W. 2019, ApJ, 873, 11810.3847/1538-4357/ab089fCrossRefGoogle Scholar