2026AJ....171...10Z

Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, along with the demand for rapid classification, present significant challenges for analysis. While machine learning offers solutions, most existing models are tailored to single tasks, struggle to generalize, and depend heavily on large, accurately labeled datasets. This paper presents an initial implementation of FALCO, a foundation model trained on Kepler light curves via self-supervised learning using a Transformer-based architecture. The model has been evaluated on Kepler data across three distinct light-curve analysis tasks, and it demonstrates robust performance in all tasks, achieving an accuracy of 95% for stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (with significantly improved precision of RMSE < 0.08 dex at the low-gravity end where log g < 1, and 0.02 dex near log g ≈ 3), and a precision of 87% in flare identification. These results highlight the versatility of the foundation model in extracting generalizable representations from light curves, enabling easy adaptation to diverse tasks and making it a promising tool for time-domain analysis. Further analysis of model scaling and input light-curve sequence length reveals that larger models and longer input sequences improve performance. We have also applied the model to produce a comprehensive catalog of surface gravity (log g) measurements for 179,732 Kepler stellar targets, using their light curves.