With Gaia currently in nominal mission mode and sending data to earth, the challenge for the astronomical community is to prepare for the use of what will be at the time of release one of the largest and most complex astronomical catalogues ever produced. Use of parallax data is not straightforward due to the presence of many statistical biases and selection effects. We present an overview of a techniques for correct use of the Gaia parallax information, which relies on statistical modelling of the data in order to infer derived quantities such as distance and absolute magnitude in an unbiased way. The methods rely on a Bayesian methodology and have been applied to case studies on normal stars, variable stars, open clusters and the LMC.

From the Gaia mission, kinematic and star count data, together with the physical parameters of the stars - ages and metallicities-, will allow to characterise our Galaxy populations and, from that, the overall Galactic gravitational potential. One of the promising procedures to reach such goal will be to optimise the present Population Synthesis models by fitting, through robust statistical techniques, the large and small scale structure and kinematics parameters that best will reproduce Gaia data. As a first step we present here the comparison between the data from the Tycho-2 catalogue and the Besançon Galaxy Model simulations, as well as our present and future work on the model optimization.

For more than ten years, a collaboration of European scientists has been producing simulations of the Gaia mission. These simulations range from detailed realistic simulated CCD images to large volume simulations of the Gaia telemetry.

The data of this decade of simulations has been used for many purposes: instrument design, data processing software testing and validation, prediction of mission performances, etc. Today the launch date is getting closer and the task of the simulator in the mission preparation will be wrapped up in a couple of years.

However, this is not the end of the story since the Gaia simulator can also be used for the preparation of the scientific exploitation of the Gaia data. In this talk we will present the simulator and its components, the data it can produce and the perspectives for the utilisation of this data during the mission, both for data evaluation and scientific exploitation.

Before Hipparcos, the determination of absolute luminosity was usually done through calibrations based on a few stellar parallaxes measured at the highest precision. The Hipparcos mission meant a giant step on the knowledge of luminosities and fine structure of the HR diagram. The Gaia mission will go an enormous step further. Besides luminosities, Gaia will allow us to derive other stellar parameters like temperature and extinction, gravity, chemical composition, age and mass by the combination of astrometric and spectrometric data. Through simulations during the mission preparation, it has been shown that the astrometric parallax information is essential to deal with the degeneracy between gravity and chemical composition ([Fe/H] and [α/Fe]), that cannot be treated using only spectrophotometry. We show the expected HR diagram for the Gaia domain and the accuracies of stellar parameters.

In this work we describe the JStuff, a preliminary generator of mock catalogues of extragalactic objects based on the Stuff code. This version is being implemented in the Java language for the ESA-Gaia satellite simulation framework. We also compare some results obtained with both versions of the simulator.

Gaia is an ambitious space astrometry mission of ESA with a main objective to map the sky in astrometry and photometry down to a magnitude 20 by the end of the next decade. While the mission is built and operated by ESA and an industrial consortium, the data processing is entrusted to a consortium formed by the scientific community, which was formed in 2006 and formally selected by ESA one year later. The satellite will downlink around 100 TB of raw telemetry data over a mission duration of 5 years from which a very complex iterative processing will lead to the final science output: astrometry with a final accuracy of a few tens of microarcseconds, epoch photometry in wide and narrow bands, radial velocity and spectra for the stars brighter than 17 mag. We discuss the general principles and main difficulties of this very large data processing and present the organization of the European Consortium responsible for its design and implementation.

The ESA space astrometry mission Gaia will measure the positions, parallaxes and proper motions of the 1 billion brightest stars on the sky. Expected accuracies are in the 7–25 μas range down to 15 mag and sub-mas accuracies at the faint limit (20 mag). The astrometric data are complemented by low-resolution spectrophotometric data in the 330–1000 nm wavelength range and, for the brighter stars, radial velocity measurements. The scientific case covers an extremely wide range of topics in galactic and stellar astrophysics, solar system and exoplanet science, as well as the establishment of a very accurate, dense and faint optical reference frame. With a planned launch around 2012 and an (extended) operational lifetime of 6 years, final results are expected around 2021. We give a brief overview of the science goals of Gaia, the overall project organisation, expected performance, and some key technical features and challenges.

The ESA HIPPARCOS satellite has provided astrometry of unprecedented accuracy, allowing us to reassess, improve and refine the pre-Hipparcos luminosity calibrations. We review the “classical” absolute magnitude calibrations with the Strömgren-Crawford intermediate-band photometric system. A small zero point correction of about 2-4% seems necessary, as well as to refine the dependences on metallicity and projected rotational velocity. The need of a rigorous statistical treatment of the extremely precise Hipparcos data to derive definitive dependences of the luminosity on physical parameters is emphasized.

GAIA will obtain, in a five-year mission, astrometric, photometric and spectroscopic data for more than a billion stars. The treatment of this huge amount of data is a challenge for the astronomical community, not only due to the database size, but also to the complex relationships existing between the different sets of data and with the instrumental and orbital parameters. The GAIA Data Analysis Study (GDAAS), under ESA's contract, aims to prove the feasibility of the data reduction scheme and to implement and test a prototype of such a large database. We present the main aspects that GDAAS addresses and review its present status.

Stellar candles are the basic tools to determine the extragalactic distance scale. Its use relies on some given fundamental relation that allows to estimate the absolute magnitude of an object from observable parameters and from that its distance. The Hipparcos mission greatly contributed to the refinement of these fundamental relations, but even with its milliarcsecond-precision data, the uncertainties in the calibration of Stellar Candles are still high. To improve these results larger samples and higher astrometric precision are needed. GAIA, with its microarcsecond, one-billion objects catalogue, will provide all the necessary data for this task.

The HIPPARCOS satellite provides astronomical data for about one thousand Long Period Variable stars (LPVs) from which kinematics properties and luminosity calibrations in several bandpasses are deduced using an appropriate method. Several results are deduced: a classification of the LPVs and its relation with the classification from the light curves, a calibration of luminosities inducing properties (age, mass, etc.) along the AGB and a comparison of oxygen to carbon-rich stars.

An extensive work on the luminosity calibration of the HR diagram has been performed using the LM method based on the Maximum Likelihood estimation (Luri et al. 1996). The method uses all available information: Hipparcos parallax and proper motion data as well as radial velocities and some measured spectroscopic or photometric parameters related to luminosity. On the other hand, the method takes into account the censorship of the sample and the errors of the data. Finally, it is able to identify and separate in a given sample, groups with different luminosity, kinematical or spatial characteristics providing not only the corresponding luminosity calibration relationship, but also improved individual absolute magnitude estimates.

In order to asses how reliable the luminosity calibrations based on the MK spectral classification are, the LM method has been applied to a sample of B to K stars with MK classification selected from the Hipparcos survey. All known binaries, variables and spectroscopically peculiar stars were removed from the sample. The final sample contains 22054 stars. Our results show that the relation between absolute magnitude and luminosity class has a large intrinsic dispersion. As a consequence, the assignation to a star of a mean absolute magnitude given by a MK classification calibration is a rough procedure. It is preferable to use the individual absolute magnitudes estimated using all the available information for each star, like the estimates provided by the LM method (Gómez et al. 1997).

This contribution is intended as a ‘rough guide’ to the Hipparcos Catalogue for the non-expert user. We shall focus here on the interpretation of the measured parallaxes in terms of distances and luminosities of stars, and on the use of the covariance matrix of the errors on the astrometric parameters. Issues not addressed here are the level at which one may expect systematic errors to occur in the Hipparcos Catalogue, and the correlation of astrometric parameters over a given region of the sky. For more details on the latter two subjects we refer to the documentation of the Hipparcos Catalogue (ESA, 1997) and to Brown et al. (1997). We note that at present the Hipparcos Catalogue contains no identified systematic errors.

In order to exploit the imminent HIPPARCOS data we have developed a new Maximum-Likelihood method to determine luminosity calibrations and kinematical properties with as bias-free as possible estimates. In most cases the samples are inhomogeneous, containing stars of several types. The method includes a way to separate the different groups. We applied the method to Mira variables contained in the HIPPARCOS Input Catalogue.

Using UBVRI photometry and proper motions we have developed a procedure to separate dwarfs and subdwarfs from stars of other luminosity classes. Three independent methods to take into account the effect of metallicity in photometric distance determinations have been applied, giving special attention to the use of infrared colours for stars with (B – V) > 0.8m.

The application of these methods to a sample of 426 stars included in the HIPPARCOS Input Catalogue –for which we have obtained photometric data– allow us to analyze systematic differences.