Astronomy and Astrophysics – Astronomy
Scientific paper
May 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009iau...261.1703h&link_type=abstract
American Astronomical Society, IAU Symposium #261. Relativity in Fundamental Astronomy: Dynamics, Reference Frames, and Data Ana
Astronomy and Astrophysics
Astronomy
Scientific paper
ESA's space astrometry mission Gaia is expected to provide the most comprehensive and accurate catalogue of stellar positions, proper motions and parallaxes for galactic and astrophysical research in the coming decades. Accurate characterization of the errors in the catalogue is essential for making optimal use of the data. Initial estimates of the standard errors for individual stars have been reported elsewhere. A less well-examined statistic is the correlation of errors in the astrometric parameters of stars as a function of their angular separation. Gaia's two widely separated fields of view minimize large-scale correlations, but there will remain some correlations on smaller angular scales. Knowledge of these is essential, e.g., when averaging over several (n) stars in a cluster. Naively, the expected improvement is by a factor n-1/2, but this rule breaks down if the errors are biased, statistically correlated, or both.
The baseline method for determining the astrometric parameters of Gaia stars is the Astrometric Global Iterative Solution (AGIS). This is an iterative least-squares estimation of the five astrometric parameters for a subset of 108 well behaved (non-variable, apparently single) stars, with additional unknowns for the spacecraft attitude, instrument calibration, and global parameters such as PPN γ. The total number of unknowns is 6×108. This large number prevents a rigorous calculation of the covariance matrix of the solution. To overcome this we estimate the correlations statistically with our light-weight and scalable simulation tool, AGISLab. Using a reduced number of stars allows us to make several solutions for different noise realizations, from which the spatial correlations can be estimated and extrapolated to the full size of AGIS. We present results of the first such analysis of expected spatial correlations in the Gaia catalogue.
Hobbs David
Holl Berry
Lindegren Lennart
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