The end-of-mission science performance of Gaia, based on CCD-level Monte Carlo simulations calibrated with measurements and then extrapolated to end-of-life conditions, has been published prior to launch in de Bruijne (2012) and was updated in de Bruijne et al. (2014) following the in-orbit commissioning phase of the mission.

Here, we summarise the most upto-date end-of-mission performance estimates based on in-orbit experience collected to date (mid 2016).

An associated Python toolkit is available from https://pypi.python.org/pypi/ PyGaia/.

The actual scientific quality of Gaia Data Release 1 is described in Lindegren et al. (2016) for the astrometry and in Evans et al. (2016) for the photometry.

All performance estimates presented here include a 20% scientific contingency margin to cover, among other things,
– scientific uncertainties and residual calibration errors in the on-ground data processing and analysis, for example uncertainties related to the spacecraft and solar system ephemeris, estimation errors in the sky background value that needs to be fed to the centroiding algorithm, the contribution to the astrometric error budget resulting from the mismatch between the actual and the calibrating point spread function, template mismatch in the spectroscopic cross-correlation, and residual errors in the derivation of the locations of the centroids of the reference spectral lines used for the spectroscopic wavelength calibration;
– the fact that the sky does not contain, as assumed for the performance assessments, perfect stars but normal stars, which
for example can be photometrically variable, have spectral peculiarities such as emission lines, have (unrecognised) companions, and can be located in crowded fields.

In the scientific performance assessments presented here, all known instrumental effects are included under the appropriate in-flight operating conditions.

Because contamination (Fig. 8) is a small effect that is kept under control as necessitated by decontamination activities, and is not easy to model, it is not included and assumed to be covered by the 20% science margin.

All error sources are included as random variables with typical deviations (as opposed to best-case or worst-case deviations).
8.1.

Astrometry The astrometric science performance of the nominal, five-year mission is usually quantified by the end-of-mission parallax standard error σϖ in units of µas.

This error, averaged over the sky and including 20% science margin (Sect. 8), depends to first order on the broadband G magnitude of Gaia,
σϖ[µas] = c(V −I) � −1.631 + 680.766z12.09 + 32.732z2 12.09, (4)
where
zx(G) = max[100.4(x−15), 100.4(G−15)],
(5) with x denoting the bright-end, noise-floor magnitude, and c(V −I) = 0.986 + (1 −0.986)(V −I). (6) The quantity c(V −I) represents a second-order V −I colour term, which quantifies the widening of the point spread function for red(der) stars (e.g. c(1) = 1 and c(3) = 1.03).

The model from Eq. (4) is valid for 3 ≤G < 20.7 mag (see Sect. 6.5 for very bright stars with G < 3 mag).

For stars that are brighter than G ≈12 mag, shortened CCD integration times, through the use of six TDI gates (number 4 with 16 TDI lines and gate numbers 8–12 with 256–2900 TDI lines; Sect. 3.3.2), are used to avoid saturation.

Each gate effectively means a different geometric instrument and necessitates a dedicated geometric calibration with associated uncertainties.

In addition, onboard magnitudeestimation errors result in a given bright star that is sometimes observed with the non-optimal TDI gate (Sect. 3.3.2).

The max function in Eq. (5) hides these complications and simply returns a constant, bright-star parallax noise floor, at σϖ = 7 µas, for stars with 3 ≤G ≤12.09 mag (assuming a G2V spectral type with V −I = 0.75 mag).

Reaching this performance, however, will require full control of all error sources and fully (iteratively) calibrated instrument and attitude models, which can only reasonably be expected in the final data release.