CAREER: Uncovering Solar Wind Composition, Acceleration, and Origin through Observations, Modeling, and Machine Learning Methods

职业：通过观测、建模和机器学习方法揭示太阳风的成分、加速度和起源

• 批准号: 2237435
• 负责人: Liang Zhao
• 金额: $ 118.45万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237435&HistoricalAwards=false
• 关键词: CAREER; Uncovering; Solar; Wind; Composition

Thirty-two years after the first sophisticated solar wind ion composition spectrometer was launched on the NASA Ulysses mission, we now have a wealth of information indicating that heavy ions play a key role in solar and heliospheric physical processes. Heavy ions act as important test particles and have unique responses to the environment around them: heavy ion composition is an imperative parameter for tracking the heliospheric structures to their sources on the Sun or to local sources in interplanetary space. As we are entering the new era of modern missions, solar wind science is at a crossroads where the science return from solar wind composition data in understanding of the inner heliosphere and beyond is maximized by integrating data, models, and machine learning techniques. This project is an innovative inter-disciplinary study to combine multiple techniques in understanding the solar wind. The broader impacts of the project include support of an early career woman scientist, support of two graduate students, the creation of annual workshops on “Heavy Ion Composition in the Heliosphere”, and outreach to Ann Arbor and Detroit area high schools.The following scientific questions will be addressed: (1) Where does the solar wind originate?; (2) How is the solar wind accelerated from the corona?; (3) How do the solar wind and heliosphere respond to the evolution of the solar cycle?; and (4) How can we better understand and use the composite solar wind data sources with Machine Learning (ML) and Artificial Intelligence (AI) technology? This research uses available in-situ observations from many instruments across multiple NASA space missions, including: NASA’s Ulysses, ACE, Wind, Parker Solar Probe, and Solar Orbiter. Space-based data will provide global solar context, magnetic field geometry and basic plasma diagnostics of the solar wind source regions. The Potential Field Source Surface (PFSS) model will be used to track the coronal magnetic field from the Sun to the Earth. In addition, ML/AI techniques will be applied to the solar wind composition data to categorize solar wind types more objectively, and to rank their importance by employing multiple ML feature selection algorithms.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.

在NASA Ulysses任务上启动了第一个复杂的太阳离子组成光谱仪之后的三十二年，我们现在拥有大量信息，表明重离子在太阳能和地层物理过程中起着关键作用。重离子充当重要的测试颗粒，并对周围的环境具有独特的响应：重离子组成是将地球层结构跟踪到其在太阳上的来源或行星际空间中的局部来源的急需参数。当我们进入现代任务的新时代时，太阳风科学正处于十字路口，从而通过整合数据，模型和机器学习技术来最大化太阳能风组成数据中的科学从太阳能风组成数据中返回。该项目是一项创新的跨学科研究，旨在结合多种技术，以理解太阳风。该项目的更广泛影响包括支持早期职业女科学家，两名研究生的支持，创建有关“ Heliosphere中重离子组成”的年度研讨会，以及向Ann Arbor和Ann Arbor和底特律地区高中的宣传。 （2）太阳能是如何从电晕加速的？ （3）太阳风和地球层如何应对太阳周期的演变？ （4）我们如何通过机器学习（ML）和人工智能（AI）技术更好地理解和使用复合太阳风数据源？这项研究使用了多个NASA空间任务中许多乐器的原位观察结果，包括：NASA的Ulysses，Ace，Wind，Parker太阳能探测器和太阳能轨道。空间数据将提供全球太阳能环境，磁场几何形状和太阳风源区域的基本等离子体诊断。潜在的场源表面（PFSS）模型将用于跟踪从太阳到地球的冠状磁场。此外，ML/AI技术将应用于太阳能风组成数据，以更客观地对太阳风类型进行分类，并通过采用多个ML功能选择算法来对其重要性进行排序。该奖项反映了NSF的法定任务，并通过评估该基金会的知识点功能和广泛的影响来评估NSF的法定任务。