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One of the central challenges to establishing the role of mergers in galaxyevolution is the selection of pure and complete merger samples in observations.In particular, while large and reasonably pure interacting galaxy pair samplescan be obtained with relative ease via spectroscopic criteria, automatedselection of post-coalescence merger remnants is restricted to the physicalcharacteristics of remnants alone. Furthermore, such selection haspredominantly focused on imaging data -- whereas kinematic data may offer acomplimentary basis for identifying merger remnants. Therefore, we examine thetheoretical utility of both the morphological and kinematic features of mergerremnants in distinguishing galaxy merger remnants from other galaxies. Deepclassification models are calibrated and evaluated using idealized syntheticimages and line-of-sight stellar velocity maps of a heterogeneous population ofgalaxies and merger remnants from the TNG100 cosmological hydrodynamicalsimulation. We show that even idealized stellar kinematic data has limitedutility compared to imaging and under-performs by $2.1\%\pm0.5\%$ incompleteness 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 nochange in purity ($0.1\%\pm0.3\%$ improvement compared to $92.7\%\pm0.2\%$,evaluated with equal numbers of merger remnant and non-remnant controlgalaxies). Classification accuracy of all models is particularly sensitive tophysical companions at separations $\lesssim40$ kpc and totime-since-coalescence. Taken together, our results show that the stellarkinematic data has little to offer in compliment to imaging for merger remnantidentification in a heterogeneous galaxy population.

The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants