SDSS-IV MaStar: theoretical atmospheric parameters for the MaNGA stellar library

We calculate the fundamental stellar parameters effective temperature, surface gravity, and iron abundance - T-eff, log g, [Fe/H] - for the final release of the Mapping Nearby Galaxies at APO (MaNGA) Stellar Library (MaStar), containing 59 266 per-visit-spectra for 24 290 unique stars at intermediate resolution (R similar to 1800) and high S/N (median = 96). We fit theoretical spectra from model atmospheres by both MARCS and BOSZ-ATLAS9 to the observed MaStar spectra, using the full spectral fitting code pPXF. We further employ a Bayesian approach, using a Markov Chain Monte Carlo (MCMC) technique to map the parameter space and obtain uncertainties. Originally in this paper, we cross match MaStar observations with Gaia photometry, which enable us to set reliable priors and identify outliers according to stellar evolution. In parallel to the parameter determination, we calculate corresponding stellar population models to test the reliability of the parameters for each stellar evolutionary phase. We further assess our procedure by determining parameters for standard stars such as the Sun and Vega and by comparing our parameters with those determined in the literature from high-resolution spectroscopy (APOGEE and SEGUE) and from lower resolution matching template (LAMOST). The comparisons, considering the different methodologies and S/N of the literature surveys, are favourable in all cases. Our final parameter catalogue for MaStar cover the following ranges: 2592 <= T-eff <= 32 983 K; -0.7 <= log g <= 5.4 dex; -2.9 <= [Fe/H] <= 1.0 dex and will be available with the last SDSS-IV Data Release, in 2021 December.

Automated summary

In this study, we calculated the fundamental stellar parameters, including effective temperature, surface gravity, and iron abundance, for the final release of the Mapping Nearby Galaxies at APO (MaNGA) Stellar Library (MaStar). We used theoretical spectra and a Bayesian approach with Markov Chain Monte Carlo techniques to obtain uncertainties in the parameter values. The results showed high reliability of the parameters for different stellar evolutionary phases.