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

太阳风包括从太阳外层发出的带电粒子和磁场。太空天气是太阳风和地球地磁场相互作用的结果。因此，了解太阳风内部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特性。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和机器学习技术。此外，还将支持一名早期职业博士后研究员。该项目由地球科学理事会共同资助，旨在支持人工智能/机器学习在地球科学领域的发展。受美国国家航空航天局帕克太阳探测器（PSP）任务的新观测结果的激励，本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，它们的相对漂移和光束速度，以及相关的离子动力学不稳定性。本文将重点研究由PSP/SPAN-I探测到的质子束对磁声子不稳定性非线性演化的影响。在离子动力学尺度上确定了离子与电磁场之间的磁波谱和能量分配。该团队将分析PSP/SPAN-I在近日点相遇期间的质子和α粒子速度分布函数（vdf）数据，以及等离子体时刻，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力波活动幅度、光谱和极化。在观测结果的指导下，研究小组将使用2.5D和3D混合粒子-细胞（hybrid-PIC）模型，在扩展盒模型中使用背景电子流体的质子和α粒子的动力学模型，来研究由初始不稳定的非麦克斯韦vdf驱动的动力学不稳定性，如超级阿尔夫萨奇光束和内部太阳风中的离子相对漂移。该模型将用于计算膨胀太阳风中质子和α粒子群的物理性质和非线性演化，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（如多层（即深度）神经网络（dnn））自动检测不稳定的vdf和分类动力学不稳定性。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。

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.