Accurate Identification of Galaxy Mergers with Stellar Kinematics
  To determine the importance of merging galaxies to galaxy evolution, it is
necessary to design classification tools that can identify different types and
stages of merging galaxies. Previously, using GADGET-3/SUNRISE simulations of
merging galaxies and linear discriminant analysis (LDA), we created an accurate
merging galaxy classifier from imaging predictors. Here, we develop a
complementary tool based on stellar kinematic predictors derived from the same
simulation suite. We design mock stellar velocity and velocity dispersion maps
to mimic the specifications of the Mapping Nearby Galaxies at Apache Point
(MaNGA) integral field spectroscopy (IFS) survey and utilize an LDA to create a
classification based on a linear combination of 11 kinematic predictors. The
classification varies significantly with mass ratio; the major (minor) merger
classifications have a mean statistical accuracy of 80% (70%), a precision of
90% (85%), and a recall of 75% (60%). The major mergers are best identified by
predictors that trace global kinematic features, while the minor mergers rely
on local features that trace a secondary stellar component. While the kinematic
classification is less accurate than the imaging classification, the kinematic
predictors are better at identifying post-coalescence mergers. A combined
imaging + kinematic classification has the potential to reveal more complete
merger samples from imaging and IFS surveys like MaNGA. We note that since the
suite of simulations used to train the classifier covers a limited range of
galaxy properties (i.e., the galaxies are intermediate mass and
disk-dominated), the results may not be applicable to all MaNGA galaxies.
