volume 12, issue S330, P275-276 2017
DOI: 10.1017/s1743921317007141
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Abstract: AbstractThe detailed study of the Galactic stellar halo may hold the key to unlocking the assembly history of the Milky Way. Here, we present a machine learning model for selecting metal poor stars from the TGAS catalogue using 5 dimensional phase-space information, coupled with optical and near-IR photometry. We characterise the degree of substructure in our halo sample in the Solar neighbourhood by measuring the velocity correlation function.