This work is done in the framework of the Gaia space mission.
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% Gaia mission overview and instruments (from GBOG proposals)
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Gaia is a cornerstone mission of the ESA Space Program, scheduled for launch in 2012. This ambitious astrometric mission has been designed to enhance our understanding of the formation and evolution of the Milky Way Galaxy.
Gaia is a scanning satellite that will repeatedly survey the whole sky to obtain positions, parallaxes and proper motions to $\mu$as precision for 10$^9$ objects brighter than $V \approx$ 20-22, depending on the spectral type.
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%Compared to its predecessor Hipparcos, Gaia will achieve a substantial improvement in terms of astrometric accuracy and completeness limit: parallax and proper-motion accuracy will be 100 times better and the number of stars is increased by a factor 10000.
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Gaia will feature two additional instruments on board: a spectrophotometric instrument consisting of a blue photometer (BP, covering 330--680\,nm), a red photometer (RP, covering 640--1050\,nm) and a medium-resolution spectrograph (Radial Velocity Spectrograph, RVS) covering 847--874\,nm which contains the Ca II IR triplet lines (hereafter CaT) and lines of several other chemical elements. These will enable the accurate measurement of radial velocities (RVs) as well as the determination of astrophysical parameters (APs) -- \Teff, \logg, \met, extinction  -- down to $V \approx 16$ and the classification of all the other targets.
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For more information about the Gaia mission, see \texttt{http://sci.esa.int/gaia/}.

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% Gaia AP determination
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The stellar characterization to be performed by Gaia in terms of \Teff, \logg\ and \met\ will allow population studies for statistically significant number of galactic stars. It is also crucial for the processing of Gaia data as such, because each type of star will receive a dedicated treatment (e.g. templates and masks used for radial velocity determination have to correspond to the nature of the object under study). Since this classification work concerns more than a billion objects, an important effort is presently committed to the development of software that will allow the automatic characterization of stars based on Gaia data.
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The common approach to carry out these determinations is to match the observed data with synthetic spectra, at low (BPRP) and higher spectral resolution (RVS). Different algorithms are presently explored, such as the use of supervised learning methods (e.g. support vector machines, artificial neural network, nearest neighbours, or k-nearest neighbour) or the application of matrix-inversion techniques (e.g. the MATISSE algorithm, \citep{2006MNRAS.370..141R}).
The AP determination is thus entirely based on model spectra, and so the results of the AP determination algorithms have to be calibrated to account for deficiencies in the physics of the models used.

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The basis for this calibration is formed by a set of benchmark stars. These are well-known, relatively bright stars for which we are compiling and obtaining, when necessary, a variety of high-quality observations (spectrophotometry, low- and high-resolution spectroscopy, interfero\-metry). In addition, we are compiling or determining fundamental atmospheric parameters (\Teff, \logg, \met) for these stars. As the vast majority of stars observed by Gaia will be of late spectral type, the following selection criteria were applied: 4000~K $\lesssim$ \Teff $\lesssim$ 6500~K; dwarfs, subgiants and red giants; solar and sub-solar metallicities. For detailed information on the benchmark star sample, see \citet{Gaia-SAM-TN}.

Each of these stars will undergo a series of model tests, i.e. a comparison of synthetic observables -- generated from atmospheric models using different assumptions for the input physics -- with the observed data.
One line of tests, which we call ``abundance tests'', consists of determining abundances for chemical elements from high-resolution spectra. Within the context of Gaia, Fe and Ca are the most relevant elements.
Different modelling assumptions will result in different abundance values, and the size of the differences will tell us the importance of each assumption for the abundance analysis procedure. In this process we will also obtain possible corrections for metallicity determinations based on a certain model.

The following types of modelling will be included in the abundance tests:
\begin{description}
   \item[A0:] 1D-LTE atmospheric models, LTE line formation, standard set of spectral lines in the optical region, line data from the VALD\footnote{\texttt{http://vald.astro.univie.ac.at}} \citep{Kupk:99}.
   \item[A1:] 1D-LTE atmospheric models, \textbf{non-LTE line formation}, otherwise same at A0.
   \item[A2:] \textbf{3D-LTE atmospheric models}, LTE line formation, otherwise same at A0.
   \item[A3:] \textbf{3D-LTE atmospheric models, non-LTE line formation}, otherwise same at A0.
   \item[A4:] 1D-LTE atmospheric models, LTE line formation, \textbf{line data} from different sources.
   \item[A5:] 1D-LTE atmospheric models, LTE line formation, \textbf{non-standard set of spectral lines}, e.g. lines in the RVS spectral region.
\end{description}

In this paper, we report a first application of this scheme: an abundance test of type A5 vs. A0, for the benchmark star $\delta$ Eri (HD 23249, HR 1136, HIP 17378, V=3.51$\pm$0.02). In particular, we compare abundances derived from two different sets of lines in two different wavelength regions.
An example for another A5-type test in the literature for the same star can be found in \citet{2005A&A...433..647A}, who used different sets of spectral lines selected based on excitation energy. To the best of our knowledge, this is the first study of $\delta$ Eri focussing on the RVS spectral region.

In Sect.~\ref{para} we discuss the fundamental parameters of $\delta$ Eri. In Sect.~\ref{obs} we describe the observations and mention some aspects of the data reduction. In Sect.~\ref{linelist} we focus on the preparation of the line list in terms of \textit{gf} calibrations using the Kurucz solar spectrum and a MARCS solar model. In Sect.~\ref{ana} we present the analysis procedure and our results for $\delta$ Eri, and Sect.~\ref{conc} completes the paper with conclusions regarding this work.
