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    Two groups of red giants with distinct chemical abundances in the bulge globular cluster NGC 6553 through the eyes of APOGEE

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    Date
    2017-02
    Author
    Tang, B
    Cohen, RE
    Geisler, D
    Schiavon, RP
    Majewski, SR
    Villanova, S
    Carrera, R
    Zamora, O
    Garcia-Hernandez, DA
    Shetrone, M
    Frinchaboy, P
    Meza, A
    Fernandez-Trincado, JG
    Munoz, RR
    Lin, CC
    Lane, RR
    Nitschelm, C
    Pan, K
    Bizyaev, D
    Oravetz, D
    Simmons, A
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    Abstract
    Multiple populations revealed in globular clusters (GCs) are important windows to the formation and evolution of these stellar systems. The metal-rich GCs in the Galactic bulge are an indispensable part of this picture, but the high optical extinction in this region has prevented extensive research. In this work, we use the high-resolution near-infrared (NIR) spectroscopic data from Apache Point Observatory Galactic Evolution Experiment (APOGEE) to study the chemical abundances of NGC 6553, which is one of the most metal-rich bulge GCs. We identify 10 red giants as cluster members using their positions, radial velocities, iron abundances, and NIR photometry. Our sample stars show a mean radial velocity of -0.14 +/- 5.47 km s(-1), land a mean [Fe/H] of -0.15 +/- 0.05. We clearly separate two populations of stars in C and N in this GC for the first time. NGC 6553 is the most metal-rich GC where the multiple stellar population phenomenon is found until now. Substantial chemical variations are also found in Na, O, and Al. However, the two populations show similar Si, Ca, and iron-peak element abundances. Therefore, we infer that the CNO, NeNa, and MgAl cycles have been activated, but the MgAl cycle is too weak to show its effect on Mg. Type Ia and Type II supernovae do not seem to have significantly polluted the second generation stars. Comparing with other GC studies, NGC 6553 shows similar chemical variations as other relatively metal-rich GCs. We also confront current GC formation theories with our results, and suggest possible avenues for improvement in the models.
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    http://repositorio.unab.cl/xmlui/handle/ria/3748
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