Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction

太阳耀斑和日冕物质抛射(CME)加速太阳高能粒子(SEP)进入日光层。当SEP指向地球时，它们会对人类技术造成影响。它们会导致卫星损坏，对宇航员造成高剂量的有害辐射，以及许多其他影响。这个项目开发了一个新的SEP预测模型，由职业早期的女性PI领导。将资助两名研究生。该项目包括一个外展计划，通过犹他州州立大学图书馆的系列讲座、大学水平的演讲和夏令营期间的太阳能工艺活动，影响犹他州从中学到大学的所有教育水平的学生。PPI还将组织一次公众Kaggle比赛，以促进SEP事件预测的科学研究。国际空间局将准备在美国州立大学图书馆举行的每月一次的“空间天气”五讲系列。此外，研究成果将作为数据挖掘相关课程的一部分，在犹他州州立大学的许多地区校园教授，其中包括位于犹他州农村地区的校园。受机器学习(ML)在物理和生命科学的多个领域取得成功的启发，该项目将实施新的先进方法来增强当前的数据集，改进SEP预测，并回答基本的科学问题。为了实现这一愿景，新模型将基于丰富的SEP父事件的时空和物理元数据来识别SEP预测特征。这些特征将从日冕物质抛射和太阳耀斑父母事件之前的活动区和日冕的图像参数和轨迹元数据中提取。新设计的特征将提高我们对快速日冕物质抛射和太阳耀斑事件(推力1)之后导致粒子加速的潜在过程的理解。该项目将解决的第二个挑战是，到目前为止，所有能量范围内总共只发生了349次SEP事件，数量很少。该项目将通过生成新的真实合成太阳事件(推力2)来扩充当前的SEP事件目录和时间序列数据集。最后，该项目将建立一个准确和稳健的集合模型，该模型结合了最先进的基于物理的模型、基于机器学习的模型和用于全能量范围SEP事件预测的新的时空方法(推力3)。这项研究的副产品之一将是一个独一无二的增强的SEP目录，补充了新的时空元数据，将通过应用编程接口(API)免费获得，用于例如训练基于机器学习和基于物理的模型。最终的业务整体模型将部署在社区协调建模中心(CCMC)，由NSF和NASA支持，并存放在公开可用的库中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Solar flares and coronal mass ejections (CMEs) accelerate solar energetic particles (SEPs) into the heliosphere. When SEPs are directed towards Earth, they cause impacts for human technology. They can cause satellite damage, high dosages of harmful radiation for astronauts, and many other effects. This project develops a new SEP prediction model, led by an early-career female PI. Two graduate students will be supported. This project includes an outreach plan to impact students at all educational levels in Utah from middle school to college level through lecture series talks in Utah State University Library, college-level presentations, and solar crafts activities during summer camp. The PI also will organize a public Kaggle competition to promote scientific research on SEP events prediction. The PI will prepare a monthly “Space Weather” five-lectures series that will take place at the USU library. Additionally, the research results will be taught as part of the data mining-related classes curriculum across many Utah State University regional campuses, which includes campuses located in rural areas of Utah. Inspired by the success of machine learning (ML) in several areas of physical and life sciences, the project will implement new advanced methods to augment current datasets, improve SEP forecasts, and answer essential scientific questions. To achieve this vision, the new model will identify SEP predictive features based on the wealth of existing spatiotemporal and physical metadata of SEP parent events. These features will be extracted from image parameters and trajectory metadata of active regions and solar prominences preceding CMEs and solar flares parents’ events. The newly engineered features will improve our understanding of the underlying processes leading to particles acceleration following fast CMEs and solar flares events (Thrust 1). The second challenge that the project will address is the small amount of SEP events that total merely 349 occurrences across all the energy ranges to this day. The project will augment the current SEP events catalog and time series dataset by generating new realistic synthetic solar events (Thrust 2). Finally, the project will build an accurate and robust ensemble model that combines state-of-the-art physics-based models, machine learning-based models, and new spatiotemporal methods for full-energy-range SEP events prediction (Thrust 3). One of the by-products of this research will be a one-of-its-own kind augmented SEP catalog supplemented with the new spatiotemporal metadata that will be freely available through Application Programming Interface (API) for its potential usage e.g., to train machine-learning-based and physics- based models. The final operational ensemble model will be deployed at the Community Coordinated Modeling Center (CCMC), supported by NSF and NASA, and deposited in publicly available repositories.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.