Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog
复制标题
基于GSEP目录的SEP事件及其可能前兆分析
DOI：
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发表时间：
2023
影响因子：
8.7
通讯作者：
P. Martens
中科院分区：
文献类型：
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作者：
Sumanth A. Rotti;P. Martens
太阳高能粒子（SEP）事件是空间天气的重要组成部分。他们的预测取决于各种因素，包括源太阳爆发，如耀斑和日冕物质抛射（CME）。对地静止太阳高能粒子（GSEP）事件目录是作为一个广泛的数据集，为这一努力的太阳活动周期22，23和24。在目前的工作中，我们审查和扩展的GSEP数据集（1）增加了“弱”SEP事件，质子增强从0.5到10 pfu在E >10 MeV的通道和（2）改善相关的太阳源喷发信息。我们分析和讨论时空属性，如耀斑的大小，位置，上升时间，速度和宽度的CME。我们检查这些参数与峰值质子通量和事件注量的相关性。我们的研究还侧重于了解特征对SEP事件预测机器学习（ML）模型最佳性能的重要性。我们在二进制分类模式中实现了随机森林，极端梯度提升，逻辑回归和支持向量机分类器。通过对最佳模型的评价，我们发现耀斑参数和CME参数对SEP事件的预测都是很好的。这项工作是我们进一步努力使用强大的ML方法进行SEP事件预测的基础。
Solar energetic particle (SEP) events are one of the most crucial aspects of space weather. Their prediction depends on various factors including the source solar eruptions such as flares and coronal mass ejections (CMEs). The Geostationary Solar Energetic Particle (GSEP) events catalog was developed as an extensive data set toward this effort for solar cycles 22, 23, and 24. In the present work, we review and extend the GSEP data set by (1) adding “weak” SEP events that have proton enhancements from 0.5 to 10 pfu in the E >10 MeV channel and (2) improving the associated solar source eruptions information. We analyze and discuss spatiotemporal properties such as flare magnitudes, locations, rise times, and speeds and widths of CMEs. We check for the correlation of these parameters with peak proton fluxes and event fluences. Our study also focuses on understanding feature importance toward the optimal performance of machine-learning (ML) models for SEP event forecasting. We implement random forest, extreme gradient boosting, logistic regression, and support vector machine classifiers in a binary classification schema. Based on the evaluation of our best models, we find both the flare and CME parameters are requisites to predict the occurrence of an SEP event. This work is a foundation for our further efforts on SEP event forecasting using robust ML methods.
DOI：
10.3389/fspas.2020.571186
发表时间：
2020-12
期刊：
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影响因子：
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作者：
M. Korsós;R. Erdélyi;Jiajia Liu;H. Morgan
通讯作者：
M. Korsós;R. Erdélyi;Jiajia Liu;H. Morgan