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

使用人工智能和机器学习技术从大型多波长数据集中识别和表征星系团，以确定是否可以从波长不完整的数据集中提取可靠的遥远星系团样本。例如，我们能计算出哪些光学/近红外选择的遥远星系团在没有获得X射线或SZ数据集的情况下具有高维氏质量吗？考虑到光学和近红外数据集很快将比来自足够灵敏的X射线和SZ实验的数据集更广泛，后一个问题尤其合适。

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.