SIMBAD references

2019A&A...629A..74G - Astronomy and Astrophysics, volume 629A, 74-74 (2019/9-1)

The LUMBA UVES stellar parameter pipeline.


Abstract (from CDS):

Context. The Gaia-ESO Survey has taken high-quality spectra of a subset of 100 000 stars observed with the Gaia spacecraft. The goal for this subset is to derive chemical abundances for these stars that will complement the astrometric data collected by Gaia. Deriving the chemical abundances requires that the stellar parameters be determined.
Aims. We present a pipeline for deriving stellar parameters from spectra observed with the FLAMES-UVES spectrograph in its standard fibre-fed mode centred on 580nm, as used in the Gaia-ESO Survey. We quantify the performance of the pipeline in terms of systematic offsets and scatter. In doing so, we present a general method for benchmarking stellar parameter determination pipelines.
Methods. Assuming a general model of the errors in stellar parameter pipelines, together with a sample of spectra of stars whose stellar parameters are known from fundamental measurements and relations, we use a Markov chain Monte Carlo method to quantitatively test the pipeline.
Results. We find that the pipeline provides parameter estimates with systematic errors on effective temperature below 100K, on surface gravity below 0.1dex, and on metallicity below 0.05dex for the main spectral types of star observed in the Gaia-ESO Survey and tested here. The performance on red giants is somewhat lower.
Conclusions. The pipeline performs well enough to fulfil its intended purpose within the Gaia-ESO Survey. It is also general enough that it can be put to use on spectra from other surveys or other spectrographs similar to FLAMES-UVES.

Abstract Copyright: © ESO 2019

Journal keyword(s): methods: numerical - stars: atmospheres - stars: statistics

Simbad objects: 24

goto Full paper

goto View the reference in ADS

To bookmark this query, right click on this link: simbad:2019A&A...629A..74G and select 'bookmark this link' or equivalent in the popup menu


© Université de Strasbourg/CNRS

    • Contact