Journal
ASTRONOMICAL JOURNAL
Volume 163, Issue 2, Pages -Publisher
IOP Publishing Ltd
DOI: 10.3847/1538-3881/ac3ca7
Keywords
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Categories
Funding
- NSF [AST-1715670]
- 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
- Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
- Korean Participation Group
- Lawrence Berkeley National Laboratory
- Yale University
- Instituto de Astrofisica de Canarias
- Johns Hopkins University
- Max-PlanckInstitut fur Astronomie (MPIA Heidelberg)
- Max-PlanckInstitut 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
- Observatorio Nacional/MCTI
- 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
- Leibniz Institut fur Astrophysik Potsdam (AIP)
- Brazilian Participation Group
- Carnegie Institution for Science
- Carnegie Mellon University
- Center for Astrophysics \ Harvard & Smithsonian (CfA)
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The MaNGA Stellar Library (MaStar) is a collection of high-quality empirical stellar spectra covering all spectral types and is used to analyze the stellar populations of galaxies observed in the MaNGA survey. In this work, physical parameters for each spectrum in the library are derived, and the uncertainties and comparisons to other analyses are presented.
The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Stellar Library (MaStar) is a large collection of high-quality empirical stellar spectra designed to cover all spectral types and ideal for use in the stellar population analysis of galaxies observed in the MaNGA survey. The library contains 59,266 spectra of 24,130 unique stars with spectral resolution R similar to 1800 and covering a wavelength range of 3622-10,354 angstrom. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (T-eff), surface gravity (log g), metallicity ([Fe/H]), microturbulent velocity (log(v(micro))) , and alpha-element abundance ([alpha/Fe]). These parameters are derived with a flexible data-driven algorithm that uses a neural network model. We train a neural network using the subset of 1675 MaStar targets that have also been observed in the Apache Point Observatory Galactic Evolution Experiment (APOGEE), adopting the independently-derived APOGEE Stellar Parameter and Chemical Abundance Pipeline parameters for this reference set. For the regions of parameter space not well represented by the APOGEE training set (7000 <= T <= 30,000 K), we supplement with theoretical model spectra. We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature.
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