Retrieving Internal Kinematics of Galaxies with Deep Learning using
  Single-Band Optical Images
  Using deep machine learning we show that the internal velocities of galaxies
can be retrieved from optical images trained using 4596 systems observed with
the SDSS-MaNGA survey. Using only $i$-band images we show that the velocity
dispersions and the rotational velocities of galaxies can be measured to an
accuracy of 29 km~$\rm{s}^{-1}$ and 69 km~$\rm{s}^{-1}$ respectively, close to
the resolution limit of the spectroscopic data. This shows that galaxy
structures in the optical holds important information concerning the internal
properties of galaxies and that the internal kinematics of galaxies are
quantitatively reflected in their stellar light distributions beyond a simple
rotational vs. dispersion distinction.
