首个专用于恒星耀斑预测的模型，融合物理属性与历史数据提升预报能力。
FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records
- 融合恒星物理特性与历史耀斑记录，通过软提示和残差融合模块建模。
- 在开普勒数据集上各项指标均优于现有方法，验证了预测有效性。
- 适合天体物理研究者、天文数据科学家及空间天气预警系统开发者。
恒星耀斑事件是天文学研究的重要观测样本，但已有记录数量有限。耀斑预测可为研究提供额外事件样本。尽管潜力巨大，此前尚无专门针对恒星耀斑预测的模型。本文通过大量实验表明，恒星物理属性与历史耀斑记录对预测任务均具价值。为此，我们提出FLARE（基于特征集成的光变曲线天文记录预测框架），首个专为恒星耀斑预测设计的大规模模型。FLARE通过新颖的软提示模块与残差记录融合模块，整合恒星物理属性与历史记录。在公开的开普勒光变曲线数据集上的实验表明，FLARE在所有评估指标上均优于其他方法。最后，通过全面案例研究验证了模型的预测能力。
原文摘要 · Abstract (English)
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.
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