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.

太阳风包括来自太阳外层发出的带电颗粒和磁场。太空天气是由太阳风与地球地磁场之间的相互作用引起的。因此，重要的是要了解太阳风中发生的详细过程。该项目通过分析卫星观测和机器学习模型的开发来探索太阳风的物理。本科生和研究生将接受跨学科研究的培训，包括太空等离子体物理和机器学习技术。此外，还将支持早​​期职业生涯后研究人员。 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动力学不稳定性。这项工作将集中于质子束的影响，由PSP/Span-I检测到对磁不稳定性的非线性演化。离子和电磁场之间的磁波光谱和能量分配将确定关注离子动力学尺度。该团队将在近调纤毛遇到期间使用梁的质子和α粒子速度分布函数（VDF）的PSP/SPS-I数据，以及诸如密度，各向异性温度和α相对抽象数据等血浆矩时的束。场仪器将提供相应的动力波活性幅度，光谱和极化。在观察结果的指导下，团队将在扩展的框模型中使用具有背景电子流体的2.5D和3D混合粒子（混合PIC）模型，以研究最初不稳定的非麦克斯韦VDF驱动的动力学不稳定型，例如超级allfvéninesolar and solar and solar and solar and Innel solar in Inter solar in Innine In In In In Inter and Inter Alar in Inner solar in Inter and Inter Arin In In Inne In In In Inne In Inner Solar。这些模型将用于计算扩展的太阳风中质子和α颗粒种群的物理性质和非线性演化，例如离子钻速度，各向异性温度，磁能和光谱以及相关的等离子体加热过程。 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的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。