Solar energetic particles (SEPs) are a major source of space radiation, especially within the inner heliosphere. These particles, originating from solar flares and coronal mass ejections (CMEs), propagate primarily along interplanetary magnetic fields. The energy spectra of SEP events are crucial for assessing radiation effects and understanding the acceleration and propagation mechanisms in their source regions. In this study, we employed a decision tree regression algorithm with cost complexity pruning to predict SEP energy spectra, including peak flux and integral fluence spectra. This approach uses only solar flares, CMEs, and solar wind data as input parameters and demonstrates strong performance to accurately predict SEP spectra. This method holds significant real-time application value for monitoring and forecasting radiation risks in both deep space and near-Earth environments.

中文翻译：

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使用机器学习回归算法预测太阳能高能粒子的能谱


太阳高能粒子 （SEP） 是空间辐射的主要来源，尤其是在日球层内。这些粒子源自太阳耀斑和日冕物质抛射 （CME），主要沿着行星际磁场传播。SEP 事件的能谱对于评估辐射效应和了解其源区域的加速和传播机制至关重要。在本研究中，我们采用决策树回归算法和成本复杂性修剪来预测 SEP 能谱，包括峰值通量和积分通量谱。这种方法仅使用太阳耀斑、CME 和太阳风数据作为输入参数，并展示了准确预测 SEP 光谱的强大性能。该方法在深空和近地环境下的辐射风险监测和预报中具有重要的实时应用价值。