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Exploring Anticorrelations and Light Element Variations in Northern Globular Clusters Observed by the APOGEE Survey

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 Added by Szabolcs Meszaros
 Publication date 2015
  fields Physics
and research's language is English




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We investigate the light-element behavior of red giant stars in Northern globular clusters (GCs) observed by the SDSS-III Apache Point Observatory Galactic Evolution Experiment (APOGEE). We derive abundances of nine elements (Fe, C, N, O, Mg, Al, Si, Ca, and Ti) for 428 red giant stars in 10 globular clusters. The intrinsic abundance range relative to measurement errors is examined, and the well-known C-N and Mg-Al anticorrelations are explored using an extreme-deconvolution code for the first time in a consistent way. We find that Mg and Al drive the population membership in most clusters, except in M107 and M71, the two most metal-rich clusters in our study, where the grouping is most sensitive to N. We also find a diversity in the abundance distributions, with some clusters exhibiting clear abundance bimodalities (for example M3 and M53) while others show extended distributions. The spread of Al abundances increases significantly as cluster average metallicity decreases as previously found by other works, which we take as evidence that low metallicity, intermediate mass AGB polluters were more common in the more metal poor clusters. The statistically significant correlation of [Al/Fe] with [Si/Fe] in M15 suggests that $^{28}$Si leakage has occurred in this cluster. We also present C, N and O abundances for stars cooler than 4500 K and examine the behavior of A(C+N+O) in each cluster as a function of temperature and [Al/Fe]. The scatter of A(C+N+O) is close to its estimated uncertainty in all clusters and independent on stellar temperature. A(C+N+O) exhibits small correlations and anticorrelations with [Al/Fe] in M3 and M13, but we cannot be certain about these relations given the size of our abundance uncertainties. Star-to-star variations of $alpha-$elements (Si, Ca, Ti) abundances are comparable to our estimated errors in all clusters.



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