Thomas Schmidt

The Large and Small Magellanic Cloud (LMC and SMC) are the most luminous dwarf galaxy satellites of the Milky Way. Thanks to their proximity (50-60 kpc), they provide one of the best opportunities to study in detail the kinematics of resolved stellar populations in an interacting pair of galaxies. Extensive photometric surveys like the ongoing Gaia mission and the near-infrared VISTA survey of the Magellanic Clouds system (VMC) have significantly impacted our insight into the Magellanic system. However, full-scale proper motion measurements and their analyses across the Magellanic Clouds are still a major challenge. In Schmidt et al. (2020), I highlighted the benefit of combining VMC and Gaia DR2 data to study the stellar kinematic in the Magellanic Bridge, connecting the LMC and the SMC, accounting for the influence of Milky Way stars. Subsequently, I have used bayesian distance estimates provided by StarHorse (Queiroz et al. 2018) to train a machine learning algorithm, which can separate Magellanic and Milky Way foreground stars more reliably than using parallax and proper motion selection criteria. With this method, it is possible to study the sparsely populated outer region of the LMC with a significantly increased sample of stars reaching below the red clump of the LMC. In this contribution, I will present results from my ongoing project dedicated to measure and analyse the proper motions of samples of stars across Magellanic regions characterised by a low stellar density and how debris from the last interaction between LMC and SMC is visible within the outer LMC.