  We use machine learning techniques to classify galaxy merger stages, which
can unveil physical processes that drive the star formation and active galactic
nucleus (AGN) activities during galaxy interaction. The sample contains 4,690
galaxies from the integral field spectroscopy survey SDSS-IV MaNGA, and can be
separated to 1,060 merging galaxies and 3630 non-merging or unclassified
galaxies. For the merger sample, there are 468, 125, 293, and 174 galaxies in
(1) incoming pair phase, (2) first pericentric passage phase, (3) aproaching or
just passing the apocenter, and (4) final coalescence phase or post-mergers.
With the information of projected separation, line-of-sight velocity
difference, SDSS gri images, and MaNGA Ha velocity map, we are able to classify
the mergers and their stages with good precision, which is the most important
score to identify interacting galaxies. For the 2-phase classification (binary;
non-merger and merger), the performance can be high (precision>0.90) with
LGBMClassifier. We find that sample size can be increased by rotation, so the
5-phase classification (non-merger, 1, 2, 3, and 4 merger stages) can be also
good (precision>0.85). The most important features come from SDSS gri images.
The contribution from MaNGA Ha velocity map, projected separation, and
line-of-sight velocity difference can further improve the performance by 0-20%.
In other words, the image and the velocity information are sufficient to
capture important features of galaxy interactions, and our results can apply to
the entire MaNGA data as well as future all-sky surveys.
