The narrative of galaxy morphological classification told through machine learning - Cheng, Ting-Yun (2020)
In this thesis, we present a complete study of machine learning applications, in- cluding both supervised and unsupervised, for galaxy morphological classification using calibrated imaging data. Two main topics are approached: (1) classification - we discuss optimal machine learning technique in terms of accuracy, efficiency, and inclusiveness using imaging data for large-scale surveys; (2) exploration - we explore galaxy morphology without human bias and discuss a novel morphological classification scheme defined by machine learning.