Speaker
Description
Galaxy morphology – the shape, size and internal structure - of a galaxy provides key insights into its properties. These includes any ongoing star formation, their merger history and other internal processes. However, this relationship becomes more complex in the dense environments of massive galaxy clusters. Here, the intracluster medium strips infalling galaxies of their gas with harassment and merging by other cluster members driving further morphological transformations. To fully understand this connection we require a large representative sample across clusters of varying mass, size and richness. We discuss using machine learning techniques with the novel ESA Datalabs platform to create such a sample. Morphology classifications of greater than 500,000 sources across 221 different clusters in the Hubble Legacy Archive are made, providing an excellent starting point for exploring the effects of the cluster environment on subtle morphologies. We discuss an initial investigation of this focused on the evolution of galaxies hosting a central bar.
