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The distribution of globular clusters in kinematic spaces does not trace the accretion history of the host galaxy (2210.04245v2)

Published 9 Oct 2022 in astro-ph.GA

Abstract: Reconstructing how all the stellar components of the Galaxy formed and assembled over time, by studying the properties of the stars which make it, is the aim of Galactic archeology. In these last years, thanks to the launch of the ESA Gaia astrometric mission, and the development of many spectroscopic surveys, we are for the first time in the position to delve into the layers of the past of our galaxy. Globular clusters (GCs) play a fundamental role in this research field since they are among the oldest stellar systems in the Milky Way (MW) and so bear witness of its entire past. In the recent years, there have been several attempts to constrain the nature of clusters (accreted or formed in the MW itself) through the analysis of kinematic spaces and to reconstruct from this the properties of the accretions events experienced by the MW through time. This work aims to test a widely-used assumption about the clustering of the accreted populations of GCs in the integrals of motions space. We analyze a set of dissipation-less N-body simulations that reproduce the accretion of one or two satellites with their GC population on a MW-type galaxy. Our results demonstrate that a significant overlap between accreted and "kinematically-heated" in-situ GCs is expected in kinematic spaces, for mergers with mass ratios of 1:10. In contrast with standard assumptions made in the literature so far, we find that accreted GCs do not show dynamical coherence, that is they do not cluster in kinematic spaces. In addition, GCs can also be found in regions dominated by stars which have a different origin (i.e. different progenitor). This casts doubt on the association between GCs and field stars that is generally made in the literature to assign them to a common origin. Our findings severely question the recovered accretion history of the MW based on the phase-space clustering of the GC population.

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