3.5.1 Overview

Author(s): Lennart Lindegren

Assessing the quality of the astrometric data is challenging, given the scarcity of independent data sets that have sufficient quality for a meaningful comparison. The quality assessment and validation must therefore to a large extent rely on various internal checks on the integrity and consistency of the data, and of the adopted models, algorithms, and software. Quality assurance for algorithms and software make use of simulations, test cases, and standard software engineering methods not further discussed here.

Broadly speaking, the quality assessment and validation methods applicable to the astrometric processing can be divided into the following categories.

1. 1.

   Basic data checks: These include checks on the amount of input and output data (e.g., what percentage of the elementary observations are actually used, and what fraction of the time do they cover) and range checks on all output quantities.

2. 2.

   Internal consistency: The astrometric processing is essentially a weighted least-squares solution and statistical tests of the residuals (including graphical output such as histograms, sky maps, and scatter plots) can be powerful method to monitor the quality and progress of the data processing and detect potential problems. Ideally, the (weighted) residuals should be unbiased, uncorrelated, Gaussian, independent of other variables, and consistent with calculated uncertainties.

3. 3.

   Cross-validation: The high redundancy of elementary observations per source makes it possible to partition the data into sets that are to a large extent statistically independent. Processing the complementary data sets separately, and comparing the astrometric results, may give a very good indication of the data quality.

4. 4.

   Model robustness and diagnostic parameters: While the astrometric model of the sources is unique and immutable, other parts of the modelling involving the nuisance parameters (for the instrument and attitude models) are to some extent arbitrary and the astrometric results should be insensitive to trivial changes e.g. of the attitude knot sequence or break times for the calibration model. Additionally, the astrometric solution may include diagnostic parameters that are expected to be zero if the basic modelling is correct. Examples are colour- and magnitude-dependent centroid displacements. Non-zero values of the diagnostic parameters indicate that the models are inadequate or insufficiently calibrated.

5. 5.

   Comparison with independent measurements: The astrometric parameters are compared with independent data sets, e.g. from other space missions (Hipparcos) or techniques (VLBI observations). Even if a strict validation of the Gaia results ideally requires external data of similar or better quality, it is in some cases possible to obtain a statistically significant assessment also when the comparison data are less precise.

6. 6.

   Astrophysical validation: This method relies on astrophysical models of certain kinds of objects. Examples are the parallaxes and proper motions of quasars, parallaxes of distant standard candles (Cepheids, RR Lyrae variables, Miras, etc.), the internal kinematics of stellar clusters, and statistical tests based on the assumed non-negativity of true parallaxes.

The following sections describe the outcome of tests made as part of the validation activities at CU3 level. In many cases the tests use data that are not part of the released data, including alternative solutions and data sets to which the final filtering was not yet applied. A number of the tests are reported in the Gaia DR2 astrometry paper (Lindegren et al. 2018), to which the reader is referred for additional details.