Investigating the morphologies of slow and fast rotators with deep learning

Abstract

The use of artificial intelligence and more precisely of Convolutional Neural Networks for the morphological classification of galaxies has been shown to be extremely reliable. In the last years, detailed kinematic studies of local early-type galaxies have revealed that a significant fraction of them seem to be rotationally supported. However, they seem to present no morphological signatures in their stellar morphology. In this work we apply machine learning to investigate the relation between kinematics and morphology of early-type galaxies. We use to that purpose SDSS imaging and spectroscopy from Manga together with simulated galaxies from the TNG100 cosmological simulation. We show that Neural Networks can distinguish fast and slow rotating early-type galaxies with 78% accuracy based exclusively on their stellar morphology even if no apparent signatures are visible to the human eye