FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy

Abstract

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. We introduce FALCO, a foundation model for astronomical light curve analysis in time-domain astronomy. This work presents the initial version of FALCO trained via self-supervised learning on unlabeled Kepler light curves using a Transformer-based architecture. The model has been evaluated on three distinct tasks and demonstrates strong generalization: achieving 95 percent accuracy in stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (notably improved to below 0.08 dex when log g is less than 1, and approximately 0.02 dex near log g equals 3), and 87 percent precision in flare identification. These results highlight the model's versatility and ability to learn generalizable representations from light curves, enabling straightforward adaptation to diverse tasks. We further analyze the impact of model scaling and sequence length, finding performance improves with larger models and longer input sequences. We also apply FALCO to derive surface gravity (log g) measurements for 179,732 Kepler stars from their light curves.

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