Ekaterina Filonenko

• In this thesis, we analyse Machine Learning methods for crystal structure detection in microgravity experiments. Our objective is to identify crystal structures of the particles by a 2D projection. We modify an already existing algorithm for 3D structures. Through extensive testing, we validate the accuracy and efficiency of our approach in various experimental conditions. Additionally, we explore the potential for integrating these methods to enhance the overall experimental workflow. Finally, we demonstrate the advantages of our modified implementations and discuss other possible approaches.