A Surrogate Model for Studying Solar Energetic Particle Transport and the Seed Population

IF 3.7 2区 地球科学 Space Weather Pub Date : 2023-12-12 DOI:10.1029/2023sw003593
Atilim Guneş Baydin, Bala Poduval, Nathan A. Schwadron
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Abstract

The high energy particles originating from the Sun, known as solar energetic particles (SEPs), contribute significantly to the space radiation environment, posing serious threats to astronauts and scientific instruments on board spacecraft. The mechanism that accelerates the SEPs to the observed energy ranges, their transport in the inner heliosphere, and the influence of suprathermal seed particle spectrum are open questions in heliophysics. Accurate predictions of the occurrences of SEP events well in advance are necessary to mitigate their adverse effects but prediction based on first principle models still remains a challenge. In this scenario, adopting a machine learning approach to SEP modeling and prediction is desirable. However, the lack of a balanced database of SEP events restrains this approach. We addressed this limitation by generating large data sets of synthetic SEP events sampled from the physics-based model, Energetic Particle Radiation Environment Module (EPREM). Using this data, we developed neural networks-based surrogate models to study the seed population parameter space. Our models, EPREM-S, run thousands to millions of times faster (depending on computer hardware), making simulation-based inference workflows practicable in SEP studies while providing predictive uncertainty estimates using a deep ensemble approach.
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研究太阳高能粒子传输和种子群的替代模型
源自太阳的高能粒子,即太阳高能粒子,对空间辐射环境的影响很大,对宇航员和航天器上的科学仪器构成严重威胁。加速sep到观测能量范围的机制、它们在日球层内部的输运以及超热种子粒子谱的影响是太阳物理学中尚未解决的问题。提前准确预测SEP事件的发生是必要的,以减轻其不利影响,但基于第一性原理模型的预测仍然是一个挑战。在这种情况下,采用机器学习方法进行SEP建模和预测是可取的。然而,缺乏一个平衡的SEP事件数据库限制了这种方法。我们通过生成从基于物理的模型高能粒子辐射环境模块(EPREM)中采样的合成SEP事件的大型数据集来解决这一限制。利用这些数据,我们建立了基于神经网络的代理模型来研究种子种群参数空间。我们的模型EPREM-S运行速度快数千到数百万倍(取决于计算机硬件),使基于模拟的推理工作流程在SEP研究中可行,同时使用深度集成方法提供预测不确定性估计。
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