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Understanding the morphological evolution of galaxies requires efficient and accu- rate classification methods, particularly in large-scale simulations and observations. This thesis presents a deep learning approach for the automated morphological classification of Large Magellanic Cloud-like galaxies, using high-resolution N-body simulations from the KRATOS suite. The study focuses on detecting s
