文章基本信息

标题：SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
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作者：Julie Imig ; Jon A.Holtzman ; Renbin Yan 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2022
卷号：163
期号：2
页码：1-18
DOI：10.3847/1538-3881/ac3ca7
语种：English
出版社：American Institute of Physics
摘要：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 ∼ 1800 and covering a wavelength range of 3622–10,354 Å. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (Teff), surface gravity (), metallicity ([Fe/H]), microturbulent velocity (), and alpha-element abundance ([α/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.