Gaia Data Release 3 Cross-match of Gaia sources with variable objects from the literature

Context. In current astronomical surveys with ever-increasing data volumes, automated methods are essential. Objects of known classes from the literature are necessary to train supervised machine-learning algorithms and to verify and validate their results. Aims. The primary goal of this work is to provide a comprehensive data set of known variable objects from the literature that we cross-match with Gaia DR3 sources, including a large number of variability types and representatives, in order to cover sky regions and magnitude ranges relevant to each class in the best way. In addition, non-variable objects from selected surveys are targeted to probe their variability in Gaia and possible use as standards. This data set can be the base for a training set that can be applied to variability detection, classification, and validation. Methods. A statistical method that employed astrometry (position and proper motion) and photometry (mean magnitude) was applied to selected literature catalogues in order to identify the correct counterparts of known objects in the Gaia data. The cross-match strategy was adapted to the properties of each catalogue, and the verification of results excluded dubious matches. Results. Our catalogue gathers 7 841 723 Gaia sources, 1.2 million of which are non-variable objects and 1.7 million are galaxies, in addition to 4.9 million variable sources. This represents over 100 variability (sub)types. Conclusions. This data set served the requirements of the Gaia variability pipeline for its third data release (DR3) from classifier training to result validation, and it is expected to be a useful resource for the scientific community that is interested in the analysis of variability in the Gaia data and other surveys.

Automated summary

The study aims to provide a comprehensive data set of known variable objects from literature that are cross-matched with Gaia DR3 sources. The data set covers various variability types and can be used for training, classification, and validation of machine-learning algorithms. Statistical methods were applied to identify the correct counterparts of known objects in the Gaia data, resulting in a catalog of 7,841,723 Gaia sources, including non-variable objects, galaxies, and variable sources.