FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records
This work addresses the need for additional flare event samples in astronomical research by providing a novel forecasting framework, though it is incremental as it applies existing techniques to a new domain.
The paper tackles the problem of forecasting stellar flares, which are limited in recorded events, by introducing FLARE, the first specialized model for this task, which integrates stellar physical properties and historical records and achieves superior performance on the Kepler dataset.
Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Our experiments on the publicly available Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.