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Kinematic analysis of the Large Magellanic Cloud using Gaia DR3

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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.

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© The Authors 2023. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. OJA acknowledges funding by l’Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) official doctoral program for the development of a R+D+i project under the FI-SDUR grant (2020 FISDU 00011). O.J.A., M.R.G., X.L., T.A. and E.M. acknowledge funding by the Spanish MICIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” by the “European Union” through Grant RTI2018- 095076-B-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia ‘María de Maeztu’) through Grant CEX2019- 000918-M. TA also acknowledges the Grant RYC2018-025968-I funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. PM acknowledges support from project Grants from the Swedish Research Council (Vetenskaprådet, Reg: 2017- 03721; 2021-04153). L.C. acknowledges financial support from the Chilean Agencia Nacional de Investigación y Desarrollo (ANID) through Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) Regular Project 1210992. SRF acknowledges financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) under Grant number AYA2016-75808-R, RTI2018-096188-B-I00, from the CAMUCM under Grant number PR65/19-22462 and the Spanish postdoctoral fellowship (2017-T2/TIC-5592).

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