%0 Generic
%A Ksoll, Victor Francisco
%C Heidelberg
%D 2021
%F heidok:30216
%R 10.11588/heidok.00030216
%T Characterising Pre-Main-Sequence Stars in the Large Magellanic Cloud with Machine and Deep Learning Techniques
%U https://archiv.ub.uni-heidelberg.de/volltextserver/30216/
%X The Large Magellanic Cloud (LMC) exhibits an extraordinary star-forming activity, providing excellent targets for star formation research. Photometric observations with the Hubble Space Telescope (HST) allow for deep, high-resolution studies of young stellar clusters and still-forming pre-main-sequence (PMS) stars in the LMC. In this thesis we study two LMC star-forming complexes, the Tarantula Nebula and N44. Using HST photometry of the Tarantula Nebula from the "Hubble Tarantula Treasury Project" (HTTP), we devise a machine-learning (ML) classification procedure to identify PMS stars from photometry and recover the PMS population captured by the HTTP survey. We introduce new HST observations of N44, the "Measuring Young Stars in Space and Time" (MYSST) survey, identify N44’s PMS content with our ML classification procedure, and conduct a clustering analysis of the identified PMS stars. Additionally, we develop a conditional invertible neural network approach to predict stellar physical parameters from photometric observations, based on the PARSEC stellar evolution models. We perform a test on HST observations of the Milky Way clusters Westerlund 2 and NGC 6397, and successfully confirm previous findings on e.g. the age of Westerlund 2. For NGC 6397, however, we identify discrepancies between the PARSEC stellar evolution models and HST observations that prevent accurate predictions.