SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar
  Library
  The 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 Mapping
Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The library
contains 59,266 spectra of 24,130 unique stars with spectral resolution
$R\sim1800$ and covering a wavelength range of $3,622-10,354$ \r{A}. In this
work, we derive five physical parameters for each spectrum in the library:
effective temperature ($T_{eff}$), surface gravity ($\log g$), metalicity
($[Fe/H]$), micro-turbulent 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 1,675 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 (ASPCAP) parameters for this reference set. For the regions
of parameter space not well represented by the APOGEE training set ($7,000 \leq
T \leq 30,000$ 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.
