Gaia is a cornerstone mission in the science programme of the European Space Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.

Context. In the era of large Galactic stellar surveys, carefully calibrating and validating the data sets has become an important and integral part of the data analysis. Moreover, new generations of stellar atmosphere models and spectral line formation computations need to be subjected to benchmark tests to assess any progress in predicting stellar properties. Aims. We focus on cool stars and aim at establishing a sample of 34 Gaia FGK benchmark stars with a range of different metallicities. The goal was to determine the effective temperature and the surface gravity independently of spectroscopy and atmospheric models as far as possible. Most of the selected stars have been subjected to frequent spectroscopic investigations in the past, and almost all of them have previously been used as reference, calibration, or test objects. Methods. Fundamental determinations of T eff and log g were obtained in a systematic way from a compilation of angular diameter measurements and bolometric fluxes and from a homogeneous mass determination based on stellar evolution models. The derived parameters were compared to recent spectroscopic and photometric determinations and to gravity estimates based on seismic data. Results. Most of the adopted diameter measurements have formal uncertainties around 1%, which translate into uncertainties in effective temperature of 0.5%. The measurements of bolometric flux seem to be accurate to 5% or better, which contributes about 1% or less to the uncertainties in effective temperature. The comparisons of parameter determinations with the literature in general show good agreements with a few exceptions, most notably for the coolest stars and for metal-poor stars. Conclusions. The sample consists of 29 FGK-type stars and 5 M giants. Among the FGK stars, 21 have reliable parameters suitable for testing, validation, or calibration purposes. For four stars, future adjustments of the fundamental T eff are required, and for five stars the log g determination needs to be improved. Future extensions of the sample of Gaia FGK benchmark stars are required to fill gaps in parameter space, and we include a list of suggested candidates.

Data Release 5 (DR5) of the Radial Velocity Experiment (RAVE) is the fifth data release from a magnitude-limited (9 < I < 12) survey of stars randomly selected in the southern hemisphere. The RAVE medium-resolution spectra (R ∼ 7500) covering the Ca-triplet region (8410-8795Å) span the complete time frame from the start of RAVE observations in 2003 to their completion in 2013. Radial velocities from 520 781 spectra of 457 588 unique stars are presented, of which 255 922 stellar observations have parallaxes and proper motions from the Tycho-Gaia astrometric solution (TGAS) in Gaia DR1. For our main DR5 catalog, stellar parameters (effective temperature, surface gravity, and overall metallicity) are computed using the RAVE DR4 stellar pipeline, but calibrated using recent K2 Campaign 1 seismic gravities and Gaia benchmark stars, as well as results obtained from highresolution studies. Also included are temperatures from the Infrared Flux Method, and we provide a catalogue of red giant stars in the dereddened color (J − Ks) 0 interval (0.50,0.85) for which the gravities were calibrated based only on seismology. Further data products for sub-samples of the RAVE stars include individual abundances for Mg, Al, Si, Ca, Ti, Fe, and Ni, and distances found using isochrones. Each RAVE spectrum is complemented by an error spectrum, which has been used to determine uncertainties on the parameters. The data can be accessed via the RAVE Web site or the Vizier database.

Context. An increasing number of high-resolution stellar spectra is available today thanks to many past and ongoing extensive spectroscopic surveys. Consequently, the scientific community needs automatic procedures to derive atmospheric parameters and individual element abundances. Aims. Based on the widely known SPECTRUM code by R.O. Gray, we developed an integrated spectroscopic software framework suitable for the determination of atmospheric parameters (i.e., effective temperature, surface gravity, metallicity) and individual chemical abundances. The code, named iSpec and freely distributed, is written mainly in Python and can be used on different platforms. Methods. iSpec can derive atmospheric parameters by using the synthetic spectral fitting technique and the equivalent width method. We validated the performance of both approaches by developing two different pipelines and analyzing the Gaia FGK benchmark stars spectral library. The analysis was complemented with several tests designed to assess other aspects, such as the interpolation of model atmospheres and the performance with lower quality spectra. Results. We provide a code ready to perform automatic stellar spectral analysis. We successfully assessed the results obtained for FGK stars with high-resolution and high signal-to-noise spectra.