Recovering the 3D Shape of Galaxies from Kinematic and Photometric Observations using Mixture Density Networks

This work aims to recover the intrinsic 3D shape of individual galaxies using their projected stellar kinematic and flux distributions through a supervised machine learning approach with mixture density networks.