Abstract
We present an automated tool for measuring atmospheric parameters (T_eff, log(g), [Fe/H]) for F-G-K dwarf and giant stars. The tool, called GAUFRE, is written in C++ and composed of several routines: GAUFRE-RV measures radial velocity from spectra via cross-correlation against a synthetic template, GAUFRE-EW measures atmospheric parameters through the classic line-by-line technique and GAUFRE-CHI2 performs a chi^2 fitting to a library of synthetic spectra. A set of F-G-K stars extensively studied in the literature were used as a benchmark for the program: their high signal-to-noise and high resolution spectra were analysed by using GAUFRE and results were compared with those present in literature. The tool is also implemented in order to perform the spectral analysis after fixing the surface gravity (log(g)) to the accurate value provided by asteroseismology. A set of CoRoT stars, belonging to LRc01 and LRa01 fields was used for first testing the performances and the behaviour of the program when using the seismic log(g).
Highlights
Spectroscopy is one of the most powerful tool that astronomy possesses in order to derive atmospheric parameters and abundances of stars
The tool is implemented in order to perform the spectral analysis after fixing the surface gravity to the accurate value provided by asteroseismology
We present the GAUFRE program, a versatile tool developed for measuring radial velocities and atmospheric parameters from optical spectra
Summary
Spectroscopy is one of the most powerful tool that astronomy possesses in order to derive atmospheric parameters and abundances of stars. By imposing excitation and ionization equilibrium through stellar atmosphere models, it is possible to derive Teff, log g and to infer elemental abundances from the curves of growth. This method is precise and it is widely adopted [1]. Another method for the estimation of atmospheric parameters and elemental abundances is to compute a set of synthetic spectra and to find the best match between the synthetic and the observed spectrum.
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