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.

Les parallaxes trigonométriques du satellite Hipparcos vont considérablement modifier de nombreux domaines de l'Astronomie. Leur précision annoncée (un facteur 5 par rapport à celles obtenues au sol) et leur nombre (100 000...) méritent une étude approfondie des erreurs externes et des éventuels effets systématiques. Après avoir décrit un modèle empirique à trois dimensions de l'absorption interstellaire qui nous a permis d'estimer la couleur d'une grande partie des étoiles du Catalogue d'Entrée d'Hipparcos, nous montrons, à l'aide des données préliminaires obtenues par le satellite, la qualité du Catalogue d'Entrée. Tirant parti des nombreuses données (au sol ou provenant d'Hipparcos) et des calibrations photométriques et spectroscopiques, cette thèse a également pour objet de mettre au point différentes méthodes qui permettront de valider statistiquement les futures parallaxes d'Hipparcos. Ces méthodes, utilisant notamment l'estimation conditionnelle pour étudier les différents biais des données, sont appliquées en détail aux parallaxes préliminaires obtenues avec un an de mission, et permettent de dégager des perspectives pour les calibrations des magnitudes absolues. Enfin, à l'aide d'analyses multivariées de données du Catalogue d'Entrée, une étude cinématique des étoiles A du voisinage solaire montre que le temps de mélange des vitesses spatiales est supérieur à deux années galactiques.

With the evolution of spacecraft systems, we see the growing need for smaller, more affordable, and robust spacecrafts that can be jettisoned with ease and sent to sites to perform a myriad of operations that a larger craft would prohibit, or that can be quickly manipulated from performing one task into another. The developing requirements have led to the creation of Nano-Satellites, or CubeSats. The question then remains, how to navigate the expanse of space with such a minute spacecraft? A solution to this is using the stars themselves as a means of navigation. This can be accomplished by measuring the distance between stars in a camera image and determining the stars' identities. Once identified, the spacecraft can obtain its position and facing. A series of star identification algorithms called Lost in Space Algorithms (LISAs) are used to recognize the stars in an image and assess the accuracy and error associated with each algorithm. This is done by creating various images from a simulated camera, using a program called MATLAB, along with images of actual stars with uncompensated errors. It is shown how suitable these algorithms are for use in space navigation, what constraints and impediments each have, and if low quality cameras using these algorithms can solve the Lost in Space problem.

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.<p><p>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.<p><p>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.<p> / Agrégation de l'enseignement supérieur, Orientation sciences / info:eu-repo/semantics/nonPublished