  Using the TNG50 cosmological simulation and observations from the Kilo-Degree
Survey (KiDS), we investigate the connection between galaxy mergers and optical
morphology in the local Universe over a wide range of galaxy stellar masses
($8.5\leqslant\log(M_\ast/\text{M}_\odot)\leqslant11$). To this end, we have
generated over 16,000 synthetic images of TNG50 galaxies designed to match KiDS
observations, including the effects of dust attenuation and scattering, and
used the $\mathrm{\mathtt{statmorph}}$ code to measure various image-based
morphological diagnostics in the $r$-band for both data sets. Such measurements
include the Gini-$M_{20}$ and concentration-asymmetry-smoothness statistics.
Overall, we find good agreement between the optical morphologies of TNG50 and
KiDS galaxies, although the former are slightly more concentrated and
asymmetric than their observational counterparts. Afterwards, we trained a
random forest classifier to identify merging galaxies in the simulation
(including major and minor mergers) using the morphological diagnostics as the
model features, along with merger statistics from the merger trees as the
ground truth. We find that the asymmetry statistic exhibits the highest feature
importance of all the morphological parameters considered. Thus, the
performance of our algorithm is comparable to that of the more traditional
method of selecting highly asymmetric galaxies. Finally, using our trained
model, we estimate the galaxy merger fraction in both our synthetic and
observational galaxy samples, finding in both cases that the galaxy merger
fraction increases steadily as a function of stellar mass.
