SHINE: Analysis of Ion Kinetic Instabilities in the Solar Wind Observed Near the Sun with Hybrid Modeling and Machine Learning

SHINE：利用混合建模和机器学习分析太阳附近观测到的太阳风中的离子动力学不稳定性

• 批准号: 2300961
• 负责人: Leon Ofman
• 金额: $ 59.89万
• 依托单位: Catholic University of America
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300961&HistoricalAwards=false
• 关键词: SHINE; Analysis; Ion; Kinetic; Instabilities

The solar wind includes charged particles and magnetic fields emanating from the Sun’s outer layers. Space weather results from interactions between the solar wind and the Earth’s geomagnetic field. Therefore, it is important to understand the detailed processes that occur within the solar wind. This project explores the physics of the solar wind through analysis of satellite observations and development of machine learning models. Undergraduate and graduate students will be trained in interdisciplinary research including space plasma physics and machine learning techniques. Also, an early career post-doctoral researcher will be supported. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.Motivated by new observations with NASA’s Parker Solar Probe (PSP) mission, the science objective of this project is to investigate the heating and acceleration of the solar wind plasma associated with proton and alpha particle temperature anisotropy evolution, their relative drift and beaming velocities, and the associated ion kinetic instabilities. The work will focus on the effects of proton beams, detected by PSP/SPAN-I on the nonlinear evolution of the magnetosonic instability. The magnetic wave spectra and energy partition between the ions and the electromagnetic fields will be determined focusing on ion kinetic scales. The team will analyze the PSP/SPAN-I data of the proton and alpha particle velocity distribution functions (VDFs) with beams during perihelia encounters, as well as plasma moments such as density, anisotropic temperature, and alpha relative abundance data. The FIELDS instrument will provide the corresponding kinetic wave activity magnitude, spectra, and polarizations. Guided by the observations, the team will use 2.5D and 3D hybrid-particle-in-cell (hybrid-PIC) models of kinetic protons and alpha particles with background electron fluid in an expanding box model to study the kinetic instabilities driven by initially unstable non- Maxwellian VDFs such as super-Alfvénic beams and ion relative drifts in the inner solar wind. The models will be used to calculate the physical properties and nonlinear evolution of the proton and alpha particle populations in the expanding solar wind, such as the ion drift speeds, anisotropic temperatures, magnetic energy and spectra, and the associated plasma heating processes. They will develop Artificial Intelligence Machine Learning (AI/ML) methods to automate the detection of unstable VDFs and classification of the kinetic instabilities using semi-supervised (i.e., labeled, and unlabeled data) and supervised (i.e., labeled data) ML methods such as multi-layered (i.e., deep) neural networks (DNNs).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.

太阳风包括从太阳外层发出的带电粒子和磁场。太空天气是太阳风与地球磁场相互作用的结果。因此，了解太阳风中发生的详细过程非常重要。该项目通过分析卫星观测和开发机器学习模型来探索太阳风的物理学。本科生和研究生将接受跨学科研究的培训，包括空间等离子体物理学和机器学习技术。此外，还将支持早​​期职业博士后研究员。该项目由地球科学局共同资助，以支持地球科学领域人工智能/机器学习的进步。在 NASA 帕克太阳探测器 (PSP) 任务的新观测结果的推动下，该项目的科学目标是研究与质子和 α 粒子温度各向异性演化相关的太阳风等离子体的加热和加速、它们的相对漂移和射束速度以及相关离子 动力学不稳定性。这项工作将重点关注 PSP/SPAN-I 检测到的质子束对磁声不稳定性非线性演化的影响。将重点关注离子动力学尺度来确定磁波谱以及离子和电磁场之间的能量分配。该团队将分析近日点遭遇期间束流的 PSP/SPAN-I 数据和 α 粒子速度分布函数 (VDF)，以及等离子体矩，例如密度、各向异性温度和 α 相对丰度数据。 FIELDS 仪器将提供相应的动波活动幅度、光谱和偏振。在观测结果的指导下，该团队将在膨胀盒模型中使用动能质子和α粒子与背景电子流体的2.5D和3D混合颗粒细胞内（hybrid-PIC）模型来研究由最初不稳定的非麦克斯韦VDF驱动的动力学不稳定性，例如超阿尔芬尼束和内太阳风中的离子相对漂移。这些模型将用于计算膨胀太阳风中质子和α粒子群的物理特性和非线性演化，例如离子漂移速度、各向异性温度、磁能和光谱以及相关的等离子体加热过程。他们将开发人工智能机器学习 (AI/ML) 方法，使用半监督（即标记和未标记数据）和监督（即标记数据）ML 方法（例如多层（即深度）神经网络 (DNN)）自动检测不稳定的 VDF 并对动力学不稳定性进行分类。该奖项反映了 通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。