We have developed a novel technique based on a clustering algorithm which searches for kinematically- and chemically-clustered stars in the APOGEE DR12 Cannon data. As compared to classical chemical tagging, the kinematic information included in our methodology allows us to identify stars that are members of known globular clusters with greater confidence. We apply our algorithm to the entire APOGEE catalog of 150,615 stars whose chemical abundances are derived by the Cannon. Our methodology found anti-correlations between the elements Al and Mg, Na and O, and C and N previously identified in the optical spectra in globular clusters, even though we omit these elements in our algorithm. Our algorithm identifies globular clusters without a priori knowledge of their locations in the sky. Thus, not only does this technique promise to discover new globular clusters, but it also allows us to identify candidate streams of kinematically- and chemically-clustered stars in the Milky Way.
@article{arxiv.1709.03987,
title = {Chemo-Dynamical Clustering applied to APOGEE data: Re-Discovering Globular Clusters},
author = {Boquan Chen and Elena D'Onghia and Stephen A. Pardy and Anna Pasquali and Clio Bertelli Motta and Bret Hanlon and Eva K. Grebel},
journal= {arXiv preprint arXiv:1709.03987},
year = {2018}
}
Comments
11 pages, 7 figures. Accepted for publication in the ApJ. Comments welcome. Information about the code can be found at: https://donghiagroup.github.io/SNN_Tagging/