Uncovering the formation of ultra-compact dwarf galaxies by multivariate statistical analysis
Tanuka Chattopadhyay, Margarita Sharina, Emmanuel Davoust, Tuli De,, Asis Kumar Chattopadhyay

TL;DR
This study uses multivariate statistical analysis to classify stellar systems, revealing six distinct groups including ultra-compact dwarf galaxies, and investigates their properties and possible origins.
Contribution
It introduces a novel application of K-Means clustering and Gap Statistics to classify hot stellar systems, providing new insights into the formation of ultra-compact dwarf galaxies.
Findings
Six distinct groups of stellar systems identified.
Ultra-compact dwarf galaxies share properties with globular clusters.
Different groups show varied mass-metallicity relations.
Abstract
We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters to giant ellipticals, which was performed in order to investigate the origin of ultra-compact dwarf galaxies. The data were mostly drawn from Forbes et al. (2008). We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light-ratios, estimated ages and metallicities. We completed the sample with globular clusters of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass…
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