SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

Imig, Julie; Holtzman, Jon A.; Yan, Renbin; Lazarz, Daniel; Chen, Yanping; Hill, Lewis; Thomas, Daniel; Maraston, Claudia; Prescott, Moire K. M.; Stringfellow, Guy S.; Bizyaev, Dmitry; Beaton, Rachael L.; Drory, Niv

doi:10.3847/1538-3881/ac3ca7

Title: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

Journal Article · 11 January 2022 · The Astronomical Journal

DOI: https://doi.org/10.3847/1538-3881/ac3ca7 · OSTI ID:1983153

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New Mexico State Univ., Las Cruces, NM (United States)
Univ. of Kentucky, Lexington, KY (United States)
New York University Abu Dhabi, (United Arab Emirates)
Univ. of Portsmouth (United Kingdom)
Univ. of Colorado, Boulder, CO (United States)
Apache Point Observatory and New Mexico State Univ., Sunspot, NM (United States); Moscow State Univ., Moscow (Russian Federation)
Carnegie Observatories, Pasadena, CA (United States)
Univ. of Texas, Austin, TX (United States)

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 (T_eff), surface gravity ($$\mathrm{log} g$$), metallicity ([Fe/H]), microturbulent velocity ($$\mathrm{log}({v}_{\mathrm{micro}})$$), 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.

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Research Organization:: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC)

OSTI ID:: 1983153

Journal Information:: The Astronomical Journal, Journal Name: The Astronomical Journal Journal Issue: 2 Vol. 163; ISSN 0004-6256

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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