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
基本信息
- 批准号:2229138
- 负责人:
- 金额:$ 80万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
了解太阳风对空间气象科学和预报至关重要,因为太阳风等离子体的特性影响地球周围空间环境的局部条件。这些条件在很大程度上是太阳风等离子体携带的速度、结构和磁场的结果。该项目解决了太阳,日光层和行星际环境(SHINE)的目标,即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太空和NSF资助的地面日冕观测的研究来了解太阳风。该项目由一位早期职业女性科学家领导,是跨学科的,建立了太阳物理学家和数据科学家之间的合作。该项目是一个为期四年的研究计划,将最先进的AI/ML技术应用于过去、当前和未来任务的太阳风原位测量。目标是对太阳风的类型进行分类,并确定其日冕源区,以了解太阳风的原地特性与其日冕起源之间的物理联系。该团队将使用美国宇航局天基任务的可用观测数据,包括高级成分探测器,风,帕克太阳探测器,太阳能探测器,以及太阳轨道器(SO)1。来自太阳和日光层观测台、日地关系观测台、日出、太阳动力学观测台和太阳观测站的光谱数据将提供太阳风源区的磁场几何形状和基本等离子体诊断。此外,NSF资助的地面日冕图,如CoMP(2011-2018),MK4(1998-2013年),KCor(2013年至今),并在可用时,UCoMP 2将用于提供额外的太阳背景数据和等离子体诊断。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进行评估,被认为值得支持的搜索.
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Depletion of Heavy Ion Abundances in Slow Solar Wind and Its Association with Quiet Sun Regions
- DOI:10.3390/universe8080393
- 发表时间:2022-07
- 期刊:
- 影响因子:2.9
- 作者:Liang Zhao;E. Landi;S. Lepri;Daniel Carpenter
- 通讯作者:Liang Zhao;E. Landi;S. Lepri;Daniel Carpenter
The S-Web Origin of Composition Enhancement in the Slow-to-moderate Speed Solar Wind
- DOI:10.3847/1538-4357/acc38c
- 发表时间:2023-03
- 期刊:
- 影响因子:0
- 作者:B. Lynch;N. Viall;A. Higginson;L. Zhao;S. Lepri;X. Sun
- 通讯作者:B. Lynch;N. Viall;A. Higginson;L. Zhao;S. Lepri;X. Sun
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Liang Zhao其他文献
Novel imidazolium stationary phase for high-performance liquid chromatography.
用于高效液相色谱的新型咪唑固定相。
- DOI:
10.1016/j.chroma.2006.03.016 - 发表时间:
2006-05 - 期刊:
- 影响因子:0
- 作者:
Hongdeng Qiu;Shengxiang Jiang;Xia Liu*;Liang Zhao - 通讯作者:
Liang Zhao
The QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction
QUENDA-BOT:木结构建筑中用于螺钉固定安装的自主机器人
- DOI:
10.1109/case56687.2023.10260465 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Dinh Dang Khoa Le;Gibson Hu;Dikai Liu;R. Khonasty;Liang Zhao;Shoudong Huang;Pratik Shrestha;R. Belperio - 通讯作者:
R. Belperio
Phenotypic effects of the nurse Thylacospermum caespitosum on dependent plant species along regional climate stress gradients
沿区域气候胁迫梯度,袋囊草保育员对依赖植物物种的表型影响
- DOI:
10.1111/oik.04512 - 发表时间:
2018-02 - 期刊:
- 影响因子:3.4
- 作者:
Xingpei Jiang;Richard Michalet;Shuyan Chen;Liang Zhao;Xiangtai Wang;Chenyue Wang;Lizhe An;Sa Xiao - 通讯作者:
Sa Xiao
Interannual variability of dimethylsulfide in the Yellow Sea
黄海二甲硫醚的年际变化
- DOI:
10.1007/s00343-020-0480-0 - 发表时间:
2022-02 - 期刊:
- 影响因子:1.6
- 作者:
Sijia Wang;Qun Sun;Shuai Li;Jiawei Shen;Qian Liu;Liang Zhao - 通讯作者:
Liang Zhao
Tape-Assisted Photolithographic-Free Microfluidic Chip Cell Patterning for Tumor Metastasis Study
用于肿瘤转移研究的胶带辅助免光刻微流控芯片细胞图案化
- DOI:
10.1021/acs.analchem.7b03225 - 发表时间:
2017 - 期刊:
- 影响因子:7.4
- 作者:
Liang Zhao;Tengfei Guo;Lirong Wang;Yang Liu;Ganyu Chen;Hao Zhou;Meiqin Zhang - 通讯作者:
Meiqin Zhang
Liang Zhao的其他文献
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{{ truncateString('Liang Zhao', 18)}}的其他基金
Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
合作研究:OAC Core:用于大规模时空预测的分布式图学习网络基础设施
- 批准号:
2403312 - 财政年份:2024
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
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 - 财政年份:2023
- 资助金额:
$ 80万 - 项目类别:
Continuing Grant
Travel: NSF Student Travel Support for the 2023 IEEE International Conference on Data Mining (IEEE ICDM 2023)
旅行:2023 年 IEEE 国际数据挖掘会议 (IEEE ICDM 2023) 的 NSF 学生旅行支持
- 批准号:
2324784 - 财政年份:2023
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
III: Small: Graph Generative Deep Learning for Protein Structure Prediction
III:小:用于蛋白质结构预测的图生成深度学习
- 批准号:
2110926 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
OAC 核心:小型:DeepJIMU:通过无梯度优化实现大规模深度学习的模型并行基础设施
- 批准号:
2007976 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation
职业:空间网络深度生成建模、转换和解释
- 批准号:
2113350 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Continuing Grant
CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors
CRII:III:使用社交传感器进行时空事件预测的可解释模型
- 批准号:
2103745 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation
职业:空间网络深度生成建模、转换和解释
- 批准号:
1942594 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Continuing Grant
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
OAC 核心:小型:DeepJIMU:通过无梯度优化实现大规模深度学习的模型并行基础设施
- 批准号:
2106446 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
III: Small: Deep Generative Models for Temporal Graph Generation and Interpretation
III:小:用于时间图生成和解释的深度生成模型
- 批准号:
2007716 - 财政年份:2020
- 资助金额:
$ 80万 - 项目类别:
Standard Grant
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