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.
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.
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
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.
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.
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.
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.