Accurate Identification of Galaxy Mergers with Imaging
  Merging galaxies play a key role in galaxy evolution, and progress in our
understanding of galaxy evolution is slowed by the difficulty of making
accurate galaxy merger identifications. We use GADGET-3 hydrodynamical
simulations of merging galaxies with the dust radiative transfer code SUNRISE
to produce a suite of merging galaxies that span a range of initial conditions.
This includes simulated mergers that are gas poor and gas rich and that have a
range of mass ratios (minor and major). We adapt the simulated images to the
specifications of the SDSS imaging survey and develop a merging galaxy
classification scheme that is based on this imaging. We leverage the strengths
of seven individual imaging predictors ($Gini$, $M_{20}$, concentration,
asymmetry, clumpiness, S\'ersic index, and shape asymmetry) by combining them
into one classifier that utilizes Linear Discriminant Analysis. It outperforms
individual imaging predictors in accuracy, precision, and merger observability
timescale (>2 Gyr for all merger simulations). We find that the classification
depends strongly on mass ratio and depends weakly on the gas fraction of the
simulated mergers; asymmetry is more important for the major mergers, while
concentration is more important for the minor mergers. This is a result of the
relatively disturbed morphology of major mergers and the steadier growth of
stellar bulges during minor mergers. Since mass ratio has the largest effect on
the classification, we create separate classification approaches for minor and
major mergers that can be applied to SDSS imaging or adapted for other imaging
surveys.
