用机器学习分析太阳耀斑与活跃区关系，找关键预测特征。
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions
- 用随机森林模型分析2011-2021年观测数据，识别影响耀斑的关键活跃区特征。
- 发现当天活跃区类型（AR_Type_Today）是最重要的预测因子，昨日哈莱分类（Hale_Class_Yesterday）最不重要。
- 差值特征（NoS_Difference）在全局和局部解释中均起关键作用，提升可解释性。
太阳耀斑是太阳表面的能量爆发，源于太阳活跃区（ARs）磁场所积累能量的突然释放。耀斑及其伴随的日冕物质抛射是空间天气的主要来源，会干扰地球附近设备，如阻断高频无线电通信、破坏电网运行。及时准确地追踪并预测耀斑对防灾减灾至关重要。本文采用随机森林（RF）模型，基于SolarMonitor.org和XRT耀斑数据库2011至2021年的观测数据，解决二分类任务，探究耀斑与其源活跃区之间的关联。目标是识别影响≥C级耀斑发生潜力的关键物理特征。研究发现，当天活跃区类型（AR_Type_Today）为最重要特征，而昨日哈莱分类（Hale_Class_Yesterday）影响力最小；无序度差异（NoS_Difference）在全局与局部解释中均具有显著决策作用。
原文摘要 · Abstract (English)
Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass ejections are sources of space weather that adversely impact devices at or near Earth, including the obstruction of high-frequency radio waves utilized for communication and the deterioration of power grid operations. Tracking and delivering early and precise predictions of solar flares is essential for readiness and catastrophe risk mitigation. This paper employs the random forest (RF) model to address the binary classification task, analyzing the links between solar flares and their originating ARs with observational data gathered from 2011 to 2021 by SolarMonitor.org and the XRT flare database. We seek to identify the physical features of a source AR that significantly influence its potential to trigger >=C-class flares. We found that the features of AR_Type_Today, Hale_Class_Yesterday are the most and the least prepotent features, respectively. NoS_Difference has a remarkable effect in decision-making in both global and local interpretations.
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