4.7 Article

Constraining ultracompact dwarf galaxy formation with galaxy clusters in the local universe

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stw498

关键词

methods: numerical; galaxies: dwarf; galaxies: formation; galaxies: interactions; galaxies: star clusters: general

资金

  1. [DP110102608]

向作者/读者索取更多资源

We compare the predictions of a semi-analytic model for ultracompact dwarf galaxy (UCD) formation by tidal stripping to the observed properties of globular clusters (GCs) and UCDs in the Fornax and Virgo clusters. For Fornax we find the predicted number of stripped nuclei agrees very well with the excess number of GCs+UCDs above the GC luminosity function. GCs+UCDs with masses >10(7.3) M-circle dot are consistent with being entirely formed by tidal stripping. Stripped nuclei can also account for Virgo UCDs with masses >10(7.3) M-circle dot where numbers are complete by mass. For both Fornax and Virgo, the predicted velocity dispersions and radial distributions of stripped nuclei are consistent with that of UCDs within similar to 50-100 kpc but disagree at larger distances where dispersions are too high and radial distributions too extended. Stripped nuclei are predicted to have radially biased anisotropies at all radii, agreeing with Virgo UCDs at clustercentric distances larger than 50 kpc. However, ongoing disruption is not included in our model which would cause orbits to become tangentially biased at small radii. We find the predicted metallicities and central black hole masses of stripped nuclei agree well with the metallicities and implied black hole masses of UCDs for masses >106.5 M-circle dot. The predicted black hole masses also agree well with that of M60-UCD1, the first UCD with a confirmed central black hole. These results suggest that observed GC+UCD populations are a combination of genuine GCs and stripped nuclei, with the contribution of stripped nuclei increasing towards the high-mass end.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据