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.

第一台精密的太阳风离子成分谱仪在美国宇航局的“重离子使命”中发射32年后，我们现在有大量的信息表明，重离子在太阳和日光层物理过程中发挥着关键作用。重离子作为重要的试验粒子，对周围环境有独特的反应：重离子成分是追踪日光层结构到其太阳源或行星际空间本地源的必要参数。随着我们进入现代任务的新时代，太阳风科学正处于一个十字路口，通过整合数据，模型和机器学习技术，从太阳风成分数据中了解日光层内外的科学回报最大化。这个项目是一个创新的跨学科研究，联合收割机多种技术在理解太阳风。该项目的更广泛影响包括支持一名早期职业女性科学家，支持两名研究生，举办关于“日光层中重离子组成”的年度讲习班，并向安阿伯和底特律地区的高中推广。(2)太阳风是如何从日冕加速的？(3)太阳风和日光层如何对太阳活动周期的演变作出反应？以及（4）如何利用机器学习（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NASA多个太空任务中许多仪器的现场观测数据，包括：NASA的太阳能探测器、ACE、Wind、帕克太阳探测器和太阳轨道器。天基数据将提供全球太阳背景、磁场几何形状和太阳风源区的基本等离子体诊断。位场源面（PFSS）模型将用于跟踪从太阳到地球的日冕磁场。此外，ML/AI技术将应用于太阳风组成数据，以更客观地对太阳风类型进行分类，并通过采用多种ML特征选择算法对其重要性进行排名。该奖项反映了NSF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。