A data-driven Labels for solar flare predictions

Solar flare prediction is a central problem in space weather forecasting. Existing solar flare prediction tools are mainly dependent on the GOES classification system, and models commonly use a proxy of maximum (peak) X-ray flux measurement over a particular prediction window to label instances. However, the background X-ray flux dramatically fluctuates over a solar cycle and often misleads both flare detection and flare prediction models during solar minimum, leading to an increase in false alarms. Our aim is to enhance the accuracy of flare prediction methods by introducing novel labeling regimes that integrate relative increases and cumulative measurements over prediction windows.太阳耀斑预测是空间天气预报中的核心问题。现有太阳耀斑预测工具主要依赖地球静止环境业务卫星（Geostationary Operational Environmental Satellite，GOES）分类系统，模型通常以特定预测窗口内的最大（峰值）X射线通量测量值作为代理标签来标注样本。然而，背景X射线通量在一个太阳活动周期内会发生剧烈波动，在太阳活动极小期时常会误导耀斑检测与耀斑预测模型，进而导致虚警率上升。本研究旨在通过引入融合预测窗口内相对增量与累积测量值的新型标注机制，提升耀斑预测方法的准确率。