The SAPP pipeline for the determination of stellar abundances and atmospheric parameters of stars in the core program of the PLATO mission
Matthew Raymond Gent, Maria Bergemann, Aldo Serenelli, Luca, Casagrande, Jeffrey M. Gerber, Ulrike Heiter, Mikhail Kovalev, Thierry Morel,, Nicolas Nardetto, Vardan Adibekyan, V\'ictor Silva Aguirre, Martin Asplund,, Kevin Belkacem, Carlos del Burgo, Lionel Bigot

TL;DR
The SAPP pipeline uses Bayesian inference to accurately determine stellar parameters and abundances for stars observed in the PLATO mission, with potential wider applications to various stellar types.
Contribution
We developed and validated the SAPP pipeline, a Bayesian-based tool for stellar parameter estimation, applicable to diverse stellar types and capable of combining multiple observables for improved accuracy.
Findings
Achieved typical uncertainties of 27 K in T_eff and 0.01 dex in log g.
Combining observables like spectra and scaling relations improves parameter accuracy.
Validated on 27 benchmark stars across different stellar types.
Abstract
We introduce the SAPP (Stellar Abundances and atmospheric Parameters Pipeline), the prototype of the code that will be used to determine parameters of stars observed within the core program of the PLATO space mission. The pipeline is based on the Bayesian inference and provides effective temperature, surface gravity, metallicity, chemical abundances, and luminosity. The code in its more general version can have a much wider range of applications. It can also provide masses, ages, and radii of stars and can be used for stars of stellar types not targeted by the PLATO core program, such as red giants. We validate the code on a set of 27 benchmark stars that includes 19 FGK-type dwarfs, 6 GK-type sub-giants, and 2 red giants. Our results suggest that combining various observables is the optimal approach, as it allows to break degeneracies between different parameters and yields more…
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