Accurate Identification of Galaxy Mergers with Stellar Kinematics
| Autor: | Jenny E. Greene, Julia M. Comerford, J. A. Vázquez-Mata, Laura Blecha, David R. Law, Rebecca Smethurst, David V. Stark, Kyle B. Westfall, Rebecca Nevin, Niv Drory, Maria Argudo-Fernández, Joel R. Brownstein |
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| Rok vydání: | 2021 |
| Předmět: |
Physics
Stellar kinematics 010504 meteorology & atmospheric sciences Velocity dispersion FOS: Physical sciences Astronomy and Astrophysics Astrophysics Kinematics Astrophysics::Cosmology and Extragalactic Astrophysics Galaxy merger Mass ratio Linear discriminant analysis 01 natural sciences Astrophysics - Astrophysics of Galaxies Galaxy Space and Planetary Science Astrophysics of Galaxies (astro-ph.GA) 0103 physical sciences Galaxy formation and evolution 010303 astronomy & astrophysics Astrophysics::Galaxy Astrophysics 0105 earth and related environmental sciences |
| DOI: | 10.48550/arxiv.2102.02208 |
| Popis: | 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. Comment: 41 pages, 21 figures, ApJ Accepted |
| Databáze: | OpenAIRE |
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