Abstract

Many observable properties of globular clusters (GCs) provide valuable insights for unveiling the hierarchical assembly of their host galaxy. For the Milky Way (MW) in particular, GCs from different accreted satellite galaxies show distinct chemical, spatial, kinematic, and age distributions. Here we examine such clustering features for model GC populations in simulated galaxies, which are carefully selected to match various observational constraints of the MW assembly. We evaluate several widely used clustering, dimensionality reduction, and supervised classification methods on these model GCs, using 10 properties that are observable in the MW. We can categorize in-situ and ex-situ formed GCs with about 90% accuracy, based solely on their clustering features in these 10 variables. The methods are also effective in distinguishing the last major merger in MW analogs with similar accuracy. Although challenging, we still find it possible to identify one, and only one, additional smaller satellite. We develop a new technique to classify the progenitors of MW GCs by combining several methods and weighting them by the validated accuracy. According to this technique, about 60% of GCs belong to the in-situ group, 20% are associated with the Gaia-Sausage/Enceladus event, and 10% are associated with the Sagittarius dwarf galaxy. The remaining 10% of GCs cannot be reliably associated with any single accretion event.

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