Kinematic analysis of the Large Magellanic Cloud using Gaia DR3
\'O. Jim\'enez-Arranz, M. Romero-G\'omez, X. Luri, P. J. McMillan, T., Antoja, L. Chemin, S. Roca-F\`abrega, E. Masana, A. Muros

TL;DR
This study uses Gaia DR3 data and a neural network classifier to analyze the internal kinematics of the Large Magellanic Cloud, revealing detailed velocity structures and the impact of contamination and assumptions on the results.
Contribution
It introduces an improved selection strategy for LMC stars using Gaia DR3 data and performs the first full 3D kinematic analysis of the LMC with this dataset.
Findings
Inner disc kinematics are dominated by the bar.
Spiral arm over-density shows inward motion and faster rotation.
Outer disc results are affected by Milky Way star contamination.
Abstract
Context: The high quality of the Gaia mission data is allowing to study the internal kinematics of the Large Magellanic Cloud (LMC) in unprecedented detail, providing insights on the non-axisymmetric structure of its disc. Aims: To define and validate an improved selection strategy to distinguish the LMC stars from the Milky Way foreground. To check the possible biases that assumed parameters or sample contamination from the Milky Way can introduce in the analysis of the internal kinematics of the LMC using Gaia data. Methods: Our selection is based on a supervised Neural Network classifier using as much as of the Gaia DR3 data as possible. We select three samples of candidate LMC stars with different degrees of completeness and purity; we validate them using different test samples and we compare them with the Gaia Collaboration paper sample. We analyse the resulting velocity profiles…
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Taxonomy
TopicsSpace Exploration and Technology · Stellar, planetary, and galactic studies
