A Neural Network Model for Predicting the Solar Energetic Particle Events

预测太阳高能粒子事件的神经网络模型

• 批准号: 2026579
• 负责人: Bala Poduval
• 金额: $ 58.57万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026579&HistoricalAwards=false
• 关键词: Neural; Network; Model; Predicting; Solar

The origin and injection of seed particles in the acceleration and transport of solar energetic particles (SEPs) in the Sun’s corona and the solar wind is still a poorly understood problem in heliophysics, thus making it a formidable task to predict with accuracy the SEP flux for the sake of reliable space weather forecast. This 3-year project aims to investigate the distribution of seed population in the solar corona and its influence in the production of SEPs. The project team will develop a Machine Learning (ML) model for the prediction of SEP events, which will be based on the Energetic Particle Radiation Environment Module (EPREM) code developed at the University of New Hampshire. The model will utilize data from the GOES, STEREO, and the Parker Solar Probe satellites, and it will adopt open-source ML libraries. This project is expected to advance our understanding of the origin and distribution of seed particles in the Sun’s corona, which influence the acceleration and transport of SEPs throughout the heliosphere. The project will develop the necessary technique and an algorithm for surrogate models that will have diverse applications in space weather forecasting. The research investigations, led by a mid-career female PI, will involve graduate students at the University of New Hampshire. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.The challenge in developing a ML model for an accurate SEP prediction is the lack of sufficient database of observed SEP events to train and validate the ML model, which is known as the “class imbalance” problem. One way to circumvent this difficulty is to employ surrogate models: that is, train the ML model on synthetic/simulated data of SEP events, and then optimize and validate the model using the observed SEP data. During this 3-year project, the team will simulate major SEP events using the EPREM code in order to explore the parameter space for the seed population; they will consider the pre-, during, and post-event spectra of the events selected for the study. The project team will also simulate time-series of SEP fluxes by incorporating these seed population parameters in order to train the ML model and then use the available SEP events database for testing. The project will make use of deep learning techniques such as LSTM as well as classification and dimensionality reduction techniques such as PCA, tSNE and autoencoder.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

太阳高能粒子（SEP）在日冕和太阳风中的加速和输运过程中种子粒子的起源和注入仍然是太阳物理学中一个不太清楚的问题，因此准确预测SEP通量是一项艰巨的任务，以实现可靠的空间天气预报。这个为期3年的项目旨在研究日冕中种子种群的分布及其对sep产生的影响。项目团队将开发一个机器学习（ML）模型，用于预测SEP事件，该模型将基于新罕布什尔大学开发的高能粒子辐射环境模块（EPREM）代码。该模型将利用GOES、STEREO和帕克太阳探测器卫星的数据，并将采用开源机器学习库。该项目预计将推进我们对日冕种子粒子起源和分布的理解，这些粒子影响太阳晕粒子在整个日球层的加速和传输。该项目将开发替代模型的必要技术和算法，这些模型将在空间天气预报中有多种应用。这项研究调查由一名职业生涯中期的女性私家侦探领导，将涉及新罕布什尔大学的研究生。该项目的研究和EPO议程支持AGS部门在发现、学习、多样性和跨学科研究方面的战略目标。开发用于准确预测SEP的ML模型的挑战在于缺乏足够的观测SEP事件数据库来训练和验证ML模型，这被称为“类不平衡”问题。规避这一困难的一种方法是使用代理模型：即，在SEP事件的合成/模拟数据上训练ML模型，然后使用观察到的SEP数据优化和验证模型。在这个为期3年的项目中，团队将使用EPREM代码模拟主要SEP事件，以探索种子种群的参数空间；他们将考虑为研究选择的事件的事前、期间和事后光谱。项目团队还将通过合并这些种子种群参数来模拟SEP通量的时间序列，以便训练机器学习模型，然后使用可用的SEP事件数据库进行测试。该项目将利用深度学习技术，如LSTM，以及分类和降维技术，如PCA， tSNE和自动编码器。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。