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Alex Bombrun

Researcher at European Space Agency

Publications -  65
Citations -  25890

Alex Bombrun is an academic researcher from European Space Agency. The author has contributed to research in topics: Astrometry & Stars. The author has an hindex of 29, co-authored 56 publications receiving 19895 citations. Previous affiliations of Alex Bombrun include Heidelberg University & Max Planck Society.

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The Averaged Control System of Fast-Oscillating Control Systems

TL;DR: In this article, the authors define an average control system that takes into account all possible variations of the control, and prove that its solutions approximate all solutions of the oscillating system as the frequency of oscillations tends to infinity.
Proceedings ArticleDOI

Gaia: focus, straylight and basic angle

TL;DR: In this article, a status review of these issues is provided, with emphasis on the mitigation schemes and the lessons learned for future space missions where extreme stability is a key requirement, and an ESA-Airbus DS working group was established during the early nominal mission and worked on a detailed root cause analysis.
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Erratum: Gaia Data Release 2: The kinematics of globular clusters and dwarf galaxies around the Milky Way (Astronomy and Astrophysics (2018) 616 (A12) DOI: 10.1051/0004-6361/201832698)

Amina Helmi, +452 more
TL;DR: In this paper, the authors correct errors in Appendix B of the Gaia Collaboration (2018) which describes the modelling of the Large and Small Magellanic Clouds (LMC and SMC) and show the rotation curve and median radial motion in the LMC.
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A conjugate gradient algorithm for the astrometric core solution of Gaia

TL;DR: In this paper, an adaptation of the classical conjugate gradient (CG) algorithm was compared to the so-called simple iteration (SI) scheme, which was previously known to converge very slowly.
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Gaia Data Release 3. The extragalactic content

Gaia Collaboration C.A.L. Bailer-Jones, +445 more
TL;DR: In this paper , the authors identify quasar and galaxy candidates via supervised machine learning methods and estimate their redshifts using the low-resolution BP/RP spectra, and further characterise the surface brightness profiles of host galaxies of quasars and of galaxies from pre-defined input lists.