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Mergers of multi-metallic globular clusters: The role of dynamics

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Amaro-Seoane,  Pau
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

Brem,  Patrick
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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1108.5173.pdf
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Citation

Amaro-Seoane, P., Konstantinidis, S., Brem, P., & Catelan, M. (2013). Mergers of multi-metallic globular clusters: The role of dynamics. Monthly Notices of the Royal Astronomical Society, 435(1), 809-821. doi:10.1093/mnras/stt1351.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0015-11E6-2
Abstract
Hubble Space Telescope observations of globular clusters (GCs) in the Antennae galaxy show clusters of clusters, or regions in the galaxy that span hundreds of parsecs, where many of the GCs are doomed to collide, and eventually merge. Several such objects appear likely to present a significant range in ages, hence possibly metallicities, and their merger could plausibly lead to multi-metallic GCs. Here we explore this process with direct-summation N-body simulations with GPU hardware. Our results reveal that colliding GCs with different metallicities and ages can produce a GC with multiplicity and occupation fractions not unlike those observed in multi-metallic clusters. In our simulations, the merged clusters have a phase with a larger amount of flattening than average, as a consequence of rapid rotation- thus suggesting that relatively recent mergers may play a role in producing highly flattened, multi-metallic clusters. We additionally explore the role of the King parameter of the cluster in the occupation fractions with a set of 160 direct-summation simulations and find that for equal size clusters the King parameter of the progenitor clusters determines the occupation fractions in the merger product, while in unequal size mergers the size of the clusters dominates the distribution of stars in the new GC. In particular, we find that the observed distribution of populations in Omega Cen can be described to some extent with our dynamical models.