Data Mining the Galatic Sky- Identifying Substructure in the Outer Parts of the Milky Way

纽伯格博士和合作者将使用斯隆数字巡天（SDSS）的数据来绘制银河系外部的地图，比以往任何时候都更完整。利用统计光度视差，合作将绘制距离太阳1.5到100千秒差距的晕星的空间分布图。他们对整个SDSS数据集的分析将使他们能够识别子结构，并从光谱分析中确定这些潮汐流中恒星的动力学和化学成分。这些研究将有助于确定形成银河系光晕的合并事件的数量和大小。潮汐流的动力学将有助于了解银河系中暗物质的空间分布。这项研究的结果将通过出版物分发给国际研究界，通过课程讲座提供给本科生，并通过公开讲座和新闻文章向公众传播。该奖项支持一名研究生和一名本科生，他们也将被鼓励在当地的K-12学校做演讲。数据挖掘活动是伦斯勒理工学院数据科学更广泛的跨学科努力的一部分。

PROPOSAL NO: AST 0307571PI: Heidi NewbergINSTITUTION: Rensselaer Polytechnic Institute Dr. Newberg and collaborators will use data from the Sloan Digital Sky Survey (SDSS) to map the outer parts of the Milky Way Galaxy more completely than has yet been possible. Using statistical photometric parallaxes, the collaboration will map the spatial distribution of halo stars at distances of 1.5 to 100 kiloparsecs from the sun. Their analysis of the full SDSS dataset will allow them to identify substructure, and determine the dynamics and chemical composition of stars in these tidal streams from spectral analysis. These studies will help to determine the number and sizes of merger events that occurred to form the halo of the Galaxy. The dynamics of the tidal streams will help understand the spatial distribution of dark matter in the Galaxy.The results from this research will be distributed to the international research community through publications, delivered to undergraduate students through course lectures and disseminated to the general public through public lectures and press articles. The award supports a graduate student and an undergraduate student, who will also be encouraged to make presentations in local K-12 schools. The data mining activities are part of a broader, interdisciplinary effort in data science at Rensselaer Polytechnic Institute.