The narrative of galaxy morphological classification told through machine learning

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. In the classification task, we first carry out a thorough comparison in accuracy and efficiency between several common supervised methods using the Dark Energy Survey (DES) imaging data (Chapter 2). The morphology labels from the Galaxy Zoo 1 (GZ1) catalogue (Lintott et al., 2008, 2011) are used to train the supervised methods. We conclude that using a combination of linear and gradient images (with the Histogram of Oriented Gradient technique) to train our convolutional neural networks (CNN) shows the most optimal performance in terms of accuracy and efficiency amongst the supervised methods tested using imaging data. Due to the better resolution (0. 263 per pixel) and greater depth (i = 22.51) of DES data than the Sloan Digital Sky survey (SDSS) imag- ing data used in the GZ1 project, we reveal that ∼ 2.5% galaxies in our dataset are mislabeled by the GZ1. After correcting these galaxies’ labels based on the DES imaging data, we reach a final accuracy of over 0.99 for binary classification (ellipticals and spirals) with the CNN (Chapter 3). We then use the CNN to build one of the largest galaxy morphological classification catalogues which in- cludes over 20 million galaxies from the DES Year 3 data (Chapter 4). However, supervised machine learning techniques are biased towards the training set and the human-defined labels. Therefore, we test the possibility of a classification task using unsupervised machine learning techniques (Cha