Automatic Morphological Classification of Galaxies: Convolutional Autoencoder and Bagging-based Multiclustering Model
ChiChun Zhou, Yizhou Gu, Guanwen Fang, Zesen Lin · The Astronomical Journal · 2022
Abstract In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of convolutional autoencoder (CAE) is used to reduce the dimensions and extract features from the imaging data; (2) the bagging-based multiclustering model is proposed to obtain the classifications with high confidence at the cost of rejecting the disputed sources that are inconsistently voted. We apply this method on the sample of galaxies with H 1010 M ⊙) are selected to investigate the connection with other physical properties. The classification scheme separates galaxies well in the U − V and V − J color space and Gini–M 20 space. The gradual tendency of Sérsic indexes and effective radii is shown from the spheroid subclass to the irregular subclass. It suggests that the combination of CAE and multiclustering strategy is an effective method to cluster galaxies with similar features and can yield high-quality morphological classifications. Our study demonstrates the feasibility of UML in morphological analysis that would develop and serve the future observations made with China Space Station telescope.