The Gaia-ESO Survey: the analysis of high-resolution UVES spectra of FGK-type stars

By R. Smiljanic, A. Korn, M. Bergemann, A. Frasca, Laura Magrini, T. Masseron, E. Pancino, G. Ruchti, I. S. Roman, L. Sbordone and 92 more. 2014

152 citations recorded by the source corpus.

Context. The ongoing Gaia-ESO Public Spectroscopic Survey is using FLAMES at the VLT to obtain high-quality medium-resolution Giraffe spectra for about 10(5) stars and high-resolution UVES spectra for about 5000 stars. With UVES, the Survey has already observed 1447 FGK-type stars. Aims. These UVES spectra are analyzed in parallel by several state-of-the-art methodologies. Our aim is to present how these analyses were implemented, to discuss their results, and to describe how a final recommended parameter scale is defined. We also discuss the precision (method-to-method dispersion) and accuracy (biases with respect to the reference values) of the final parameters. These results are part of the Gaia-ESO second internal release and will be part of its first public release of advanced data products. Methods. The final parameter scale is tied to the scale defined by the Gaia benchmark stars, a set of stars with fundamental atmospheric parameters. In addition, a set of open and globular clusters is used to evaluate the physical soundness of the results. Each of the implemented methodologies is judged against the benchmark stars to define weights in three different regions of the parameter space. The final recommended results are the weighted medians of those from the individual methods. Results. The recommended results successfully reproduce the atmospheric parameters of the benchmark stars and the expected T-eff-log g relation of the calibrating clusters. Atmospheric parameters and abundances have been determined for 1301 FGK-type stars observed with UVES. The median of the method-to-method dispersion of the atmospheric parameters is 55K for T-eff, 0.13dex for log g and 0.07 dex for [Fe/H]. Systematic biases are estimated to be between 50-100 K for T-eff, 0.10-0.25 dex for log g and 0.05-0.10 dex for [Fe/H]. Abundances for 24 elements were derived: C, N, O, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Mo, Ba, Nd, and Eu. The typical method-to-method dispersion of the abundances varies between 0.10 and 0.20 dex. Conclusions. The Gaia-ESO sample of high-resolution spectra of FGK-type stars will be among the largest of its kind analyzed in a homogeneous way. The extensive list of elemental abundances derived in these stars will enable significant advances in the areas of stellar evolution and Milky Way formation and evolution.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/66b2bc2bfec1597dc072c23be0519fd0d9620327

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Citations in the corpus

The Payne: Self-consistent ab initio Fitting of Stellar Spectra (2018, background): “Surveys such as APOGEE (Majewski et al. 2017), GALAH (De Silva et al. 2015), and Gaia–ESO (Smiljanic et al. 2014) are collecting high-quality spectra for 105–106 stars with a spectral resolution R; 25,000, orders of magnitudes more stars than previous samples.”
SPECTRAL PROPERTIES OF COOL STARS: EXTENDED ABUNDANCE ANALYSIS OF 1,617 PLANET-SEARCH STARS (2016, background): “These low to medium resolution spectroscopic surveys are deriving stellar parameters for tens of thousands to millions of stars with smaller numbers of higher resolution observations for calibration (De Silva et al. 2015; Holtzman et al. 2015; Smiljanic et al. 2014).”
THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION (2015, methodology): “Even when the input assumptions are held fixed, differences in the employed analysis methods lead to substantial differences in assigned labels (e.g. Smiljanic et al. 2014).”
The GALAH Survey: Second Data Release (2018, methodology): “While micro-turbulence is updated in every iteration, it is dictated by the empirical formulae that have been calibrated for the LUMBA node of the Gaia-ESO survey (Smiljanic et al. 2014), which uses a similar SME-based analysis pipeline as ours.”