Classifying MaNGA Velocity Dispersion Profiles by Machine Learning

Abstract

We present a machine learning (ML) approach for classifying kinematic profiles of elliptical galaxies in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Previous studies employing ML to classify spectral data of galaxies have provided valuable insights into morphological galaxy classification. This study aims to enhance the understanding of galaxy kinematics by leveraging ML. The kinematics of 2,624 MaNGA elliptical galaxies are investigated using integral field spectroscopy (IFS) by classifying their one-dimensional velocity dispersion (VD) profiles. We utilised a total of 1,266 MaNGA VD profiles and employed a combination of unsupervised and supervised learning techniques. The unsupervised K-means algorithm classifies VD profiles into four categories: flat, decline, ascend, and irregular. A bagged decision trees classifier (TreeBagger) supervised ensemble is trained using visual tags, achieving 100% accuracy on the training set and 88% accuracy on the test set. Our analysis identifies the majority (68%) of MaNGA elliptical galaxies presenting flat VD profiles, which requires further investigation into the implications of the Dark Matter problem.