Thèse de doctorat
Résumé : Building upon its success with the Hipparcos space astrometry mission launched in 1989, the European Space Agency has agreed to fund the construction of its successor, Gaia, and its launch in 2011. Despite the similarities between the two missions, Gaia will be orders of magnitude more powerful, more sensitive, but also more complex in terms of data processing. Growing from 120,000 stars with Hipparcos to about 120,000E4 stars with Gaia does not simply mean pushing the computing resources to their limits (1 second of processing per star yields 38 years for the whole Gaia-sky). It also means facing situations that did not occur with Hipparcos either by luck or because those cases were carefully removed from the Hipparcos Input Catalogue.

This manuscript illustrates how some chunks of the foreseen Gaia data reduction pipeline can be trained and assessed using the Hipparcos observations. This is especially true for unresolved binaries because they pop up so far down in the Gaia pipeline that, by the time they get there, there is essentially no difference between Hipparcos and Gaia data. Only the number of such binaries is different, going from two thousand to ten million.

Although the computing time clearly becomes an issue, one cannot sacrifice the robustness and correctness of the reduction pipeline for the sake of speed. However, owing to the requirement that everything must be Gaia-based (no help from ground-based results), the very robustness of the reduction has to be assessed as well. For instance, the underlying assumptions of some statistical tests used to assess the quality of the fits used in the Hipparcos pipeline might no longer hold with Gaia. That may not affect the fit itself but rather the quality indicators usually accompanying those fits. For the final catalogue to be a success, these issues must be addressed as soon as possible.