Variability-selected active galactic nuclei in the VST-SUDARE/VOICE survey of the COSMOS field

Context. Active galaxies are characterized by variability at every wavelength, with timescales from hours to years depending on the observing window. Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. Aims. In the present work we test the use of optical variability as a tool to identify active galactic nuclei in the VST multiepoch survey of the COSMOS field, originally tailored to detect supernova events. Methods. We make use of the multiwavelength data provided by other COSMOS surveys to discuss the reliability of the method and the nature of our AGN candidates. Results. The selection on the basis of optical variability returns a sample of 83 AGN candidates; based on a number of diagnostics. we conclude that 67 of them are confirmed AGNs (81% purity), 12 are classified as supernovae, while the nature of the remaining 4 is unknown. For the subsample of AGNs with some spectroscopic classification, we find that Type 1 are prevalent (89%) compared to Type 2 AGNs (11%). Overall, our approach is able to retrieve on average 15% of all AGNs in the field identified by means of spectroscopic or X-ray classification, with a strong dependence on the source apparent magnitude (completeness ranging from 26% to 5%). In particular, the completeness for Type 1 AGNs is 25%, while it drops to 6% for Type 2 AGNs. The rest of the X-ray selected AGN population presents on average a larger rms variability than the bulk of non variable sources. indicating that variability detection for at least some of these objects is prevented only by the photometric accuracy of the data. The low completeness is in part due to the short observing span: we show that increasing the temporal baseline results in larger samples as expected for sources with a red-noise power spectrum. Our results allow us to assess the usefulness of this AGN selection technique in view of future wide field surveys.