Accurate Identification of Galaxy Mergers with Stellar Kinematics

TL;DR

This paper develops a stellar kinematic-based classifier for galaxy mergers using simulations and compares its effectiveness to imaging-based methods, aiming to improve merger identification in surveys like MaNGA.

Contribution

It introduces a new kinematic predictor-based classification tool for galaxy mergers, complementing existing imaging methods, and assesses its performance across different merger types.

Findings

Kinematic classifiers achieve 80% accuracy for major mergers.

Minor merger classification accuracy is around 70%.

Combining imaging and kinematic data enhances merger detection.

Abstract

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.