Exploring Young Stellar Variability with Gaia DR3 light curves

Abstract

Context: Photometric variability is a defining characteristic of young stellar objects (YSO), which can be traced back to a range of physical processes taking place at different stages of young stars' formation and early evolution. Gaia's third Data Release (GDR3) has provided an unprecedented dataset of photometric time series, including 79375 light curves for YSO candidates. With 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 further and verify the recurrence of YSO variability modes due to accretion, extinction, rotation modulation, etc. By adapting the Q&M methodology for Gaia's sparse and long-term light curves, we seek to bridge the gap between low and high-cadence surveys' insights on YSO variability. Methods: We piloted the application of the asymmetry (M) and periodicity (Q) variability metrics to characterise YSO variability with Gaia light curves. Through refined sample selection, we identified sources with appropriate sampling for the Q&M methodology. We used the Generalised Lomb Scargle periodogram and structure functions to infer variability timescales. Results: We successfully derived Q&M indices for ~23000 sources in the GDR3 YSO sample. These variables were then classified into eight variability morphological classes. We linked morphological classes with physical mechanisms by using H$α$ as a proxy of accretion and $α_\mathrm{IR}$-indices to gauge circumstellar material's presence. Conclusions: We demonstrate that the Q&M metrics can be successfully applied to Gaia's sparse time-series. We applied them to distinguish between the several variability modes. While our results are generally consistent with previous high-cadence, short-term studies, we find that GDR3's long timespan yields an enhanced variety of variability mechanisms.

Figures (33)

• Figure 1: Sky distribution of sources with GDR3 epoch data (YSO and GAPS samples). The location of the GAPS field is highlighted by an ellipse and a label in magenta. The distributions are shown in Galactic coordinates and in an Aitoff projection. In the top panel, colours indicate the source density, and in the bottom, colours reflect the average number of epochs for the light curves given their sky position.
• Figure 2: Workflow of filtering and cleaning steps to remove extragalactic contamination prior to asymmetry and periodicity analysis (Sect. \ref{['sec:extragalactic_filtering']}), sources with too few epochs (Sect. \ref{['sec:numb_epochs']}), affected by a spurious scan-angle (Sect. \ref{['sec:spurious_var']}), outliers (Sect. \ref{['sec:spurious_outliers']}), and, finally, sorting the sources based on their degree of variability (Sect. \ref{['sec:selection_high_var']}).
• Figure 3: Standard deviation versus $G$ magnitude for light curves in the GAPS (top) and YSO (bottom) samples. The colour bar indicates source counts. The solid blue line marks the 97th percentile for the GAPS sample, and the hatched region identifies YSO sources lying below this percentile (secondary sample), while primary-sample sources lie above it. In both panels, the black dashed line shows the lower envelope (1st rolling percentile) of the YSO distribution to aid visual comparison.
• Figure 4: Workflow for deriving the characteristic timescale required for the periodicity analysis with the Q index.
• Figure 5: Example GLS periodograms used to derive periodic timescales.The upper panel shows a periodogram with a peak above the 1% FAL, from which a periodic timescale is obtained, while the lower panel shows a case with no such peak, where an aperiodic timescale is instead derived from the structure function.