Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 509, Issue 3, Pages 4024-4036Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab3089
Keywords
catalogues; surveys; galaxies: disc; galaxies: elliptical; lenticular, cD; galaxies: photometry; galaxies: structure
Categories
Funding
- NSF [AST-1816330]
- project: Cross-field research in space sciences [PIE2018-50E099]
- European Union ERDF
- Comunitat Valenciana
- Alfred P. Sloan Foundation
- U.S. Department of Energy Office of Science
- Center for High Performance Computing at the University of Utah
- Chilean Participation Group
- French Participation Group
- Instituto de Astrofisica de Canarias
- The Johns Hopkins University
- Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
- Korean Participation Group
- Lawrence Berkeley National Laboratory
- Leibniz Institut fur Astrophysik Potsdam (AIP)
- Max-Planck-Institut fur Astronomie (MPIA Heidelberg)
- Max-Planck-Institut fur Astrophysik (MPA Garching)
- Max-Planck-Institut fur Extraterrestrische Physik (MPE)
- National Astronomical Observatories of China
- New Mexico State University
- New York University
- University of Notre Dame
- Observatario 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
- Carnegie Institution for Science
- Carnegie Mellon University
- Harvard-Smithsonian Center for Astrophysics
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This paper presents the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey. The MPP-VAC-DR17 provides photometric parameters from Sersic and Sersic+Exponential fits to the two-dimensional surface brightness profiles of the MaNGA DR17 galaxy sample, while the MDLM-VAC-DR17 provides deep-learning-based morphological classifications for the same galaxies, with improvements compared to previous releases.
We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sersic and Sersic+Exponential fits to the two-dimensional surface brightness profiles of the MaNGA DR17 galaxy sample in the g, r, and i bands (e.g. total fluxes, half-light radii, bulge-disc fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides deep-learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties, for example, a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies that were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): Namely, the low end of the T-Types is better recovered in this new version. The catalogue also includes a separation between early or late type, which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (-1 < T-Type < 2), where the T-Type values show a large scatter. In addition, k-fold-based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined.
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