期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 483, 期 2, 页码 2057-2077出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/sty3135
关键词
galaxies: fundamental parameters; galaxies: photometry; galaxies: structure
资金
- NSF [AST-1816330]
- Alfred P. Sloan Foundation
- U.S. Department of Energy Office of Science
- Center for High-Performance Computing at the University of Utah
- Carnegie Institution for Science
- Carnegie Mellon University
- Chilean Participation Group
- French Participation Group
- Harvard-Smithsonian Center for Astrophysics
- Instituto de Astrofisica de Canarias
- Johns Hopkins University
- Kavli Institute for the Physics and Mathematics of the Universe/University of Tokyo
- Lawrence Berkeley National Laboratory
- Leibniz Institut fur Astrophysik Potsdam
- Max-Planck-Institut fur Astronomie (Heidelberg)
- Max-Planck-Institut fur Astrophysik (Garching)
- Max-Planck-Institut fur Extraterrestrische Physik
- National Astronomical Observatories of China
- New Mexico State University
- New York University
- University of Notre Dame
- Observatorio Nacional/MCTI
- The Ohio State University
- Pennsylvania State University
- Shanghai Astronomical Observatory
- United Kingdom Participation Group
- Universidad Nacional Autonoma de Mexico
- University of Arizona
- University of Colorado Boulder
- University of Oxford
- University of Portsmouth
- University of Utah
- University of Virginia
- University of Washington
- University of Wisconsin
- Vanderbilt University
- Yale University
- Brazilian Participation Group
We describe the Sloan Digital Sky Survey IV (SDSS-IV) MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) PyMorph Photometric (MPP-VAC) and MaNGA Deep Learning Morphology (MDLM-VAC) Value Added Catalogues. The MPP-VAC provides photometric parameters from Sersic and Sersic + Exponential fits to the 2D surface brightness profiles of the MaNGA Data Release 15 (DR15) galaxy sample. Compared to previous PYMORPH analyses of SDSS imaging, our analysis of the MaNGA DR15 incorporates three improvements: the most recent SDSS images; modified criteria for determining bulge-to-disc decompositions; and the fits in MPP-VAC have been eye-balled, and re-fit if necessary, for additional reliability. A companion catalogue, the MDLM-VAC, provides Deep Learning-based morphological classifications for the same galaxies. The MDLM-VAC includes a number of morphological properties (e.g. a TType, and a finer separation between elliptical and S0 galaxies). Combining the MPP- and MDLM-VACs allows to show that the MDLM morphological classifications are more reliable than previous work. It also shows that single-Sersic fits to late- and early-type galaxies are likely to return Sersic indices of n <= 2 and >= 4, respectively, and this correlation between n and morphology extends to the bulge component as well. While the former is well known, the latter contradicts some recent work suggesting little correlation between n-bulge and morphology. Combining both VACs with MaNGA's spatially resolved spectroscopy allows us to study how the stellar angular momentum depends on morphological type. We find correlations between stellar kinematics, photometric properties, and morphological type even though the spectroscopic data played no role in the construction of the MPP- and MDLM-VACs.
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