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arXiv 1806.06341astro-ph.GA
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StarGO:一种确定晕星起源的新方法

StarGO: A New Method to Identify the Galactic Origins of Halo Stars

Zhen Yuan, Jiang Chang, Projjwal Banerjee, Jiaxin Han, Xi Kang, M. C. Smith

摘要:

我们发展了一种新的方法StarGO(Stars' Galactic Origin),利用晕星的运动学来识别晕星的星系起源。我们的方法是基于自组织映射(SOM),这是最流行的无监督学习算法之一。StarGO将SOM与基本上没有自由参数的新型自适应组识别算法相结合。为了评估我们的模型,我们建立了一个合成的恒星晕合并的九个卫星在银河系。我们构建模拟目录提取的日心体积为10千秒差距从我们的模拟和分配预期的观测不确定性对应的明亮的恒星从盖亚DR2和LAMOST DR5。我们比较的结果,从StarGO对朋友的友谊(FoF)为基础的方法在空间的轨道能量和角动量。我们表明,StarGO能够系统地识别更多的卫星,并实现更高的数量分数的识别恒星的大多数卫星内提取的体积。当应用于盖亚DR2的数据时,StarGO将使我们能够以前所未有的细节揭示内部恒星晕的起源。

英文摘要:

We develop a new method StarGO (Stars' Galactic Origin) to identify the galactic origins of halo stars using their kinematics. Our method is based on self-organizing map (SOM), which is one of the most popular unsupervised learning algorithms. StarGO combines SOM with a novel adaptive group identification algorithm with essentially no free parameters. In order to evaluate our model, we build a synthetic stellar halo from mergers of nine satellites in the Milky Way. We construct the mock catalogue by extracting a heliocentric volume of 10 kpc from our simulations and assigning expected observational uncertainties corresponding to bright stars from Gaia DR2 and LAMOST DR5. We compare the results from StarGO against that from a Friends-of-Friends (FoF) based method in the space of orbital energy and angular momentum. We show that StarGO is able to systematically identify more satellites and achieve higher number fraction of identified stars for most of the satellites within the extracted volume. When applied to data from Gaia DR2, StarGO will enable us to reveal the origins of the inner stellar halo in unprecedented detail.

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