Exploring the variability of young stars with Gaia DR3 light curves

Context. Photometric variability is a defining characteristic of young stellar objects (YSOs) that can be traced back to a range of physical processes that occur at different stages in the formation and early evolution of young stars. The Gaia third Data Release (GDR3) has provided an unprecedented dataset of photometric time series, including 79 375 light curves for sources classified as YSO candidates. Through its all-sky coverage, Gaia provides a unique opportunity for large-scale studies of YSO variability. Aims. Our goal was to characterise the GDR3 sample of YSO variables to better identify the recurrence of YSO variability modes (caused by accretion, extinction, rotation modulation, etc.). We made a pilot study of the applicability of the asymmetry (M) and periodicity (Q) variability metrics to characterise YSO variability with Gaia light curves. By adapting the Q-M metrics for sparse and long-term light curves, we sought to bridge the gap between low- and high-cadence survey insights on YSO variability. Methods. We adapted the Q-M method for Gaia. Through a refined sample selection, we identified sources with an appropriate sampling for the Q-M method. We used the generalised Lomb Scargle periodogram and structure functions to derive characteristic variability timescales. Results. We successfully derived Q-M indices for 23 000 sources in the GDR3 YSO sample. These variables were then classified into eight variability morphological classes. We linked the morphological classes with physical mechanisms using Hα as a proxy of accretion and αIR indices to gauge whether circumstellar material was present. Conclusions. We demonstrate that the Q-M metrics can be successfully applied to study the sparse time series of Gaia. We applied it successfully to distinguish between the various variability modes of YSOs. While our results are generally consistent with previous high-cadence short-term studies, the long GDR3 time span yields a larger variety of variability mechanisms.