Crossmatching variable objects with the Gaia data

Tens of millions of new variable objects are expected to be identified in over a billion time series from theGaiamission. Crossmatching known variable sources with those from Gaia is crucial to incorporate current knowledge, understand how these objects appear in the Gaia data, train supervised classifiers to recognise known classes, and validate the results of the Variability Processing and Analysis Coordination Unit (CU7) within the Gaia Data Analysis and Processing Consortium (DPAC). The method employed by CU7 to crossmatch variables for the first Gaia data release includes a binary classifier to take into account positional uncertainties, proper motion, targeted variability signals, and artefacts present in the early calibration of the Gaia data. Crossmatching with a classifier makes it possible to automate all those decisions which are typically made during visual inspection. The classifier can be trained with objects characterized by a variety of attributes to ensure similarity in multiple dimensions (astrometry, photometry, time-series features), with no need for a-priori transformations to compare different photometric bands, or of predictive models of the motion of objects to compare positions. Other advantages as well as some disadvantages of the method are discussed. Implementation steps from the training to the assessment of the crossmatch classifier and selection of results are described. Introduction. The crossmatch of celestial objects makes it possible to combine complementary information from data collected at various epochs, with different observational and instrumental features (such as wavebands, time sampling, duration, sky coverage, photometric and astrometric accuracy), and also to extract new information by leveraging the synergy among data sets. At the same time, some of the differences in instrumentation and data taking, convolved with the properties of the objects to crossmatch (herein named targets), can lead to misses and false detections (Gray et al. 2007), which can become numerous as the number of targets grows. Common causes of crossmatch errors include large positional uncertainties, proper motion, variability, blended objects, spurious sources, detector edges or gaps, contamination, noise, etc. Variable objects can be more challenging to crossmatch than constant sources, but they also provide additional features which can be exploited to aid in the identification of correct matches. For each object, we consider multiple characteristics derived from astrome-