The combined and respective roles of imaging and stellar kinematics in
  identifying galaxy merger remnants
  One of the central challenges to establishing the role of mergers in galaxy
evolution is the selection of pure and complete merger samples in observations.
In particular, while large and reasonably pure interacting galaxy pair samples
can be obtained with relative ease via spectroscopic criteria, automated
selection of post-coalescence merger remnants is restricted to the physical
characteristics of remnants alone. Furthermore, such selection has
predominantly focused on imaging data -- whereas kinematic data may offer a
complimentary basis for identifying merger remnants. Therefore, we examine the
theoretical utility of both the morphological and kinematic features of merger
remnants in distinguishing galaxy merger remnants from other galaxies. Deep
classification models are calibrated and evaluated using idealized synthetic
images and line-of-sight stellar velocity maps of a heterogeneous population of
galaxies and merger remnants from the TNG100 cosmological hydrodynamical
simulation. We show that even idealized stellar kinematic data has limited
utility compared to imaging and under-performs by $2.1\%\pm0.5\%$ in
completeness and $4.7\%\pm0.4\%$ in purity for our fiducial model architecture.
Combining imaging and stellar kinematics offers a small boost in completeness
(by $1.8\%\pm0.4\%$, compared to $92.7\%\pm0.2\%$ from imaging alone) but no
change in purity ($0.1\%\pm0.3\%$ improvement compared to $92.7\%\pm0.2\%$,
evaluated with equal numbers of merger remnant and non-remnant control
galaxies). Classification accuracy of all models is particularly sensitive to
physical companions at separations $\lesssim40$ kpc and to
time-since-coalescence. Taken together, our results show that the stellar
kinematic data has little to offer in compliment to imaging for merger remnant
identification in a heterogeneous galaxy population.
