Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T14:42:37.941Z Has data issue: false hasContentIssue false

Inter-cluster velocity structures of star cluster complexes

Published online by Cambridge University Press:  11 March 2020

Michiko S. Fujii*
Affiliation:
Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan email: [email protected]
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.

Star clusters are often born as star-cluster systems, which include several stellar clumps. Such star-cluster complexes could have formed from turbulent molecular clouds. Since Gaia Data Release 2 provided us high quality velocity data of individual stars in known star-cluster complexes, we now can compare the velocity structures of the observed star-cluster complexes with simulated ones. We performed a series of N-body simulations for the formation of star-cluster complexes starting from turbulent molecular clouds. We measured the inter-cluster velocity dispersions of our simulated star-cluster complexes and compared them with the Carina region and NGC 2264. We found that the Carina region and NGC 2264 formed from molecular clouds with a mass of ∼4 × 105M and ∼4 × 104M, respectively. In our simulations, we also found that the maximum cluster mass (Mc,max) in the complex follows ${M_{{\rm{c}},{\rm{max}}}} = 0.{\rm{2}}0M_g^{0.76}$, where Mg is the initial gas mass.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Eisenstein, D. J. & Hut, P. 1998, ApH, 498, 137Google Scholar
Fujii, M., Iwasawa, M., Funato, Y., et al. 2007, PASJ, 59, 1095CrossRefGoogle Scholar
Fujii, M. S. & Portegies Zwart, S. 2011, Science, 334, 1380CrossRefGoogle Scholar
Fujii, M. S. & Portegies Zwart, S. 2015, MNRAS, 449, 726CrossRefGoogle Scholar
Fujii, M. S. 2015, PASJ, 67, 59CrossRefGoogle Scholar
Fujii, M. S. & Portegies Zwart, S. 2016, ApJ, 817, 4CrossRefGoogle Scholar
Fujii, M. S. 2019, MNRAS, 486, 3019CrossRefGoogle Scholar
Collaboration, Gaia, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1Google Scholar
Hughes, A., Meidt, S. E., Colombo, D., et al. 2013, ApJ, 779, 46CrossRefGoogle Scholar
Kuhn, M. A., Hillenbrand, L. A., Sills, A., et al. 2019, ApJ, 870, 32CrossRefGoogle Scholar
Nitadori, K. & Makino, J. 2008, New Atron., 13, 498CrossRefGoogle Scholar
Pelupessy, F. I., van der Werf, P. P., & Icke, V. 2004, A&A, 422, 55Google Scholar
Pelupessy, F. I., van Elteren, A., de Vries, N., et al. 2013, A&A, 557, A84Google Scholar
Sakurai, Y., Yoshida, N., Fujii, M. S., et al. 2017, MNRAS, 472, 1677CrossRefGoogle Scholar
Saitoh, T. R. & Makino, J. 2013, ApJ, 768, 44CrossRefGoogle Scholar
Sana, H., Momany, Y., Gieles, M., et al. 2010, A&A, 515, A26Google Scholar