SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation

SHINE：通过新颖的人工智能研究了解太阳风的原位特性和日冕起源的物理联系

• 批准号: 2229138
• 负责人: Liang Zhao
• 金额: $ 80万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229138&HistoricalAwards=false
• 关键词: SHINE; Understanding; Physical; Connection; situ; SHINE; Understanding; Physical; Connection; situ

Understanding the solar wind is crucial to space weather science and forecasting because the properties of the solar wind plasma affect the local conditions in the space environment around Earth. These conditions are largely the result of the speed, structure, and magnetic fields carried by the solar wind plasma. This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar wind, through research that utilizes modern Artificial Intelligence (AI), Machine Learning (ML) and big data analysis algorithms to analyze space-based and NSF-funded ground based coronagraph observations. The project is led by an early career female scientist and is cross-disciplinary, building a collaboration between solar physicists and data scientists. Graduate and undergraduate student researchers from under-represented groups in STEM will be supported.The project is a four-year research program that applies state-of-the-art AI/ML technology to in-situ solar wind measurements from past, current, and future missions. The goal is to classify solar wind types and to determine their coronal source regions, to understand the physical connection between the solar wind’s in-situ properties and their coronal origins. The team will use available observations from NASA space-based missions including the Advanced Composition Explorer, Wind, Parker Solar Probe, Ulysses and when available, Solar Orbiter (SO) 1. Spectroscopic data from the Solar and Heliospheric Observatory, Solar Terrestrial Relations Observatory, Hinode, Solar Dynamics Observatory and SO will provide magnetic field geometry and basic plasma diagnostics of the solar wind source regions. Furthermore, NSF-funded ground based coronagraphs such as CoMP (2011-2018), MK4 (1998-2013), KCor (2013-today) and, when available, UCoMP2 will be used to provide additional solar context data and plasma diagnostics.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.

了解太阳风对于太空天气科学和预测至关重要，因为太阳风血浆的特性会影响地球周围空间环境中的当地条件。这些条件在很大程度上是太阳风血浆携带的速度，结构和磁场的结果。该项目通过利用现代人工智能（AI），机器学习（ML）和大数据分析算法来分析基于空间和NSF资助的基于NSF资助的基于地面的Coronagraph观察的研究，解决了理解太阳风的太阳能，地球环和星际环境（Shine）的目标。该项目由早期职业女科学家领导，并且是跨学科的，建立了太阳能物理学家和数据科学家之间的合作。将支持来自STEM中代表性不足小组的研究生和本科生研究人员。该项目是一项为期四年的研究计划，该计划将最先进的AI/ML技术应用于过去，当前和未来任务的原位太阳风测量。目的是对太阳风类型进行分类并确定其冠状动脉源区域，以了解太阳风的原位特性与其冠状起源之间的物理联系。该团队将使用NASA基于空间的任务的可用观察结果，包括高级构图探索者，风，帕克太阳能探针，尤利西斯，以及如果可用的话，太阳能轨道（SO）1。来自太阳能和地层观测值的光谱数据。地区。 Furthermore, NSF-funded ground based coronagraphs such as CoMP (2011-2018), MK4 (1998-2013), KCor (2013-today) and, when available, UCoMP2 will be used to provide additional solar context data and plasma diagnostics.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review 标准。