Author(s): B. Nevin, J. Comerford, Astrophysics and Planetary Sciences, University of Colorado Boulder, Boulder, Colorado, UNITED STATES|L. Blecha, University of Florida, Gainesville, Florida, UNITED STATES|J. Greene, Princeton University, Princeton, New Jersey, UNITED STATES|
Institution(s): 1. Astrophysics and Planetary Sciences, University of Colorado Boulder, Boulder, CO, United States. 2. University of Florida, Gainesville, FL, United States. 3. Princeton University, Princeton, NJ, United States.
Contributing team(s): (none)
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. Mergers are typically identified using individual imaging techniques, each of which has its own limitations and biases. With the growing popularity of integral field spectroscopy (IFS), it is now possible to introduce kinematic signatures to improve galaxy merger identifications. We use GADGET-3 N-body/hydrodynamics simulations of merging galaxies coupled with SUNRISE dust radiative transfer simulations to create mockup IFS and images to match the specifications of SDSS-IV’s MaNGA (Mapping Nearby Galaxies at Apache Point) survey. From the mockup galaxies, we have developed the first merging galaxy classification scheme that is based on kinematics and imaging. Utilizing a Linear Discriminant Analysis tool, we use a linear combination of kinematic and imaging predictors to identify merging galaxies from the > 10,000 galaxies in the MaNGA survey, identifying many more mergers than possible before. Through the accurate identification of merging galaxies in the MaNGA survey, we will advance our understanding of supermassive black hole growth in galaxy mergers and other open questions related to galaxy evolution.