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

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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.