Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies

TL;DR

This study compares galaxy morphologies from the TNG50 simulation and KiDS observations, using machine learning to identify mergers and analyze how merger fractions vary with stellar mass across a broad range of galaxy types.

Contribution

It introduces a method combining synthetic imaging, morphological diagnostics, and machine learning to accurately identify galaxy mergers in simulations and observations.

Findings

Good agreement between simulated and observed galaxy morphologies.

Asymmetry is the most important feature for merger identification.

Merger fraction increases with stellar mass in both data sets.

Abstract

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.