Identify and characterise the properties of galaxy clusters in large multi-wavelength data sets

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Identification and characterisation of galaxy clusters from large multi-wavelength data sets using AI and machine learning techniques in order to determine whether reliable samples of distant clusters can be extracted from wavelength-incomplete data sets. For example, can we work out which of the optical/near-IR selected distant cluster candidates have high virialised masses without access to X-ray or SZ data sets? This latter question is particularly apposite given that the optical & near-IR datasets will soon be more extensive than those from sufficiently sensitive X-ray and SZ experiments.