, for example capable of discovering many thousands of supernovae over the course of its lifetime (e.g.

Hodgkin et al. 2013).

Therefore, DPAC runs a system that uses the IDT outputs, including source positions (already accurate to better than 100 mas) and fluxes, to build up a history of the observed sky to enable the discovery of transient phenomena, for which spectro-photometry is immediately available at each epoch.

The candidate transients are then filtered and the most interesting candidates are published as alerts, including the relevant Gaia data to enable rapid follow-up with ground-based telescopes.

The science alerts system has been tested during an extended validation campaign (leading, for example, to the discovery of an eclipsing AM CVn system; Campbell et al. 2015), and is now routinely producing alerts that can be accessed through http://gsaweb.ast.cam.ac.uk/alerts.

Solar system alerts This system processes the daily IDT outputs to search for new solar system objects (mostly main-belt asteroids and near-Earth objects) that can be recognised by their fast motion across the sky (a typical main-belt asteroid moves at ∼10 mas s−1 with respect to the stars; Tanga et al. 2016).

At the instantaneous measurement precision of Gaia, these objects can be seen to move on the sky between successive scans by Gaia across the same region, and in a fraction of the cases the motion can be detected during a focal plane transit.

The observations of candidate moving objects are matched to each other and preliminary orbits are determined.

If it is established that an unknown solar system object is found, the orbit is used to predict where it should appear on the sky over the weeks following its discovery.

This information is published as an alert (and will be made available through https://gaiafunsso.imcce.fr/) to enable ground-based follow-up observations.

These observations are essential to establish an accurate orbit of the newly discovered object by observing it over a longer time baseline because, depending on the Gaia actual orbit and scanning law, it may never be observed by Gaia again.

More details can be found in Tanga et al. (2016).
7.2.

Cyclic data processing The self-calibrating nature of Gaia (Sect. 3.1) is reflected in the iterative processing of the data, which aims to derive both the source parameters and the calibration (or nuisance) parameters that together best explain the raw observations.

This iterative or cyclic processing is bootstrapped by the initial data treatment in the daily systems (Sect. 7.1) and subsequently proceeds by iterating the following steps:
1.

Update the basic calibrations, such as the point spread function (PSF) model (including detector charge-transferinefficiency effects), wavelength calibrations, the CCD-PEM non-uniformity calibration, the straylight and background model, etc. 2.

Reprocess all the raw observations using the latest calibrations.

This step includes the improvement of the matching of Gaia observations to sources (which includes the creation of new sources where necessary), that is the cross-match.

3.

Use the results from the preceding preprocessing step to derive improved astrometry, photometry, and spectroscopy for each source.

Steps 1 and 2 above both take into account the most up-to-date source parameters, attitude model, geometric calibrations, etc., thus closing the iterative loop.

A concrete example to illustrate the processing steps above is the iteration between PSF modelling and astrometry.

A given PSF model combined with predicted source positions in the focal plane (which involves the source astrometry, the spacecraft attitude, and the geometric calibration) can be used to predict the observed sample values of the source image.

The comparison to the actual sample values can be used to improve the model.

Conversely, the improved PSF model can be used to derive an improved estimate of the image location and the image flux from the raw sample values.

In both steps, the source colour should also be accounted for to properly calibrate the chromatic shifts of source images (see Sect. 3.3.1).

The following cyclic processing tasks execute the three steps above:
Intermediate Data Update (IDU) This task (Castañeda et al. 2015) updates core calibrations, such as the PSF model, and improves the source to observation cross-matching.

Subsequently it repeats all higher level functions of IDT (in particular the estimation of astrometric image parameters) on the (unchanging) raw astrometric and photometric observations, using better geometric calibrations, attitude and astrometric source parameters from AGIS, and source colours from the photometric pipeline.

The results of the updated crossmatch form the basis of the source list used by all the DPAC processing systems, including the daily systems described above.