Kinematic analysis of the Large Magellanic Cloud using Gaia DR3^⋆,⋆⋆

¹ Departament de Física Quàntica i Astrofísica (FQA), Universitat de Barcelona (UB), C Martí i Franqués, 1, 08028 Barcelona, Spain
² Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain
e-mail: ojimenez@icc.ub.edu
³ Institut d’Estudis Espacials de Catalunya (IEEC), C Gran Capità, 2-4, 08034 Barcelona, Spain
⁴ Lund Observatory, Department of Astronomy and Theoretical Physics, Lund University, Box 43 22100 Lund, Sweden
⁵ Centro de Astronomía – CITEVA, Universidad de Antofagasta, Avenida Angamos 601, Antofagasta 1270300, Chile
⁶ Departamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, Plaza de las Ciencias, 1, 08028 Madrid, Spain
⁷ Instituto de Astronomía, Universidad Nacional Autónoma de México, Apartado Postal 106, 22800 Ensenada, B.C., Mexico

Received: 26 July 2022
Accepted: 1 October 2022

Abstract

Context. The high quality of the Gaia mission data has allowed for studies of the internal kinematics of the Large Magellanic Cloud (LMC) to be undertaken in unprecedented detail, providing insights into the non-axisymmetric structure of its disc. Recent works by the Gaia Collaboration have already made use of the excellent proper motions of Gaia DR2 and Gaia EDR3 for a first analysis of this sort, but these were based on limited strategies aimed at distinguishing the LMC stars from the Milky Way foreground that did not use all the available information. In addition, these studies could not access the third component of the stellar motion, namely, the line-of-sight velocity – which has now become available via Gaia DR3 for a significant number of stars.

Aims. Our aim is twofold: 1) to define and validate an improved, more efficient and adjustable selection strategy to distinguish the LMC stars from the Milky Way foreground; 2) to check the possible biases that assumed parameters or sample contamination from the Milky Way can introduce in analyses of the internal kinematics of the LMC based on Gaia data.

Methods. Our selection was based on a supervised neural network classifier, using as much as of the Gaia DR3 data as possible. Based on this classifier, we selected three samples of candidate LMC stars with different degrees of completeness and purity. We validated these classification results using different test samples and we compared them with the results from the selection strategy used in the Gaia Collaboration papers, based only on the proper motions. We analysed the resulting velocity profiles and maps for the different LMC samples and we checked how these results change when we use the line-of-sight velocities that are available for a subset of stars.

Results. We show that the contamination in the samples from Milky Way stars basically affects the results for the outskirts of the LMC. We also show that the analysis formalism used in absence of line-of-sight velocities does not bias the results for the kinematics in the inner disc. Here, for the first time, we performed a kinematic analysis of the LMC using samples with the full three dimensional (3D) velocity information from Gaia DR3.

Conclusions. The detailed 2D and 3D kinematic analysis of the LMC internal dynamics demonstrate that: 1) the dynamics in the inner disc is mainly bar dominated; 2) the kinematics on the spiral arm overdensity seems to be dominated by an inward motion and a rotation that is faster than that of the disc in the part of the arm attached to the bar; 3) the contamination of Milky Way stars seem to dominate the outer parts of the disc and mainly affects old evolutionary phases; and 4) uncertainties on the assumed disc morphological parameters and line-of-sight velocity of the LMC can (in some cases) have significant effects on the results of the analysis.