3D Modelling of the Large Magellanic Cloud using Markov Chain Monte Carlo

Abstract

The Large Magellanic Cloud (LMC) is one of the closest galaxies to the Milky Way, at a distance of about 50 kpc. With the latest Gaia Data Release, Gaia DR3, and its unprecedented precision and number of stars, the study of the galaxy can reach new depths. However, the uncertainty in the parallax for LMC stars is of the same order of magnitude of the values themselves, making the use of individual measurements to determine distance and structure unfeasible. In this work we will study the possibility of exploiting DR3 through the use of a Markov-Chain Monte Carlo (MCMC) algorithm. By studying the algorithm’s ability to return parameters used to generate synthetic models of the LMC with varying uncertainties, we will assess the feasibility of the use of Gaia parallaxes in the 3D modelling of the LMC structure.