EarthCube Data Capabilities: Machine Learning Enhanced Cyberinfrastructure for Understanding and Predicting the Onset of Solar Eruptions

EarthCube 数据功能：机器学习增强的网络基础设施，用于理解和预测太阳喷发的发生

• 批准号: 1927578
• 负责人: Haimin Wang
• 金额: $ 84.12万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927578&HistoricalAwards=false
• 关键词: EarthCube; Data; Capabilities; Machine; Learning

Space weather is a term used to describe changing environmental conditions in the solar system caused by eruptions on the Sun's surface such as solar flares. Understanding and forecasting of solar eruptions is critically important for national security and for the economy since they are known to have adverse effects on critical technology infrastructure such as satellite and power distribution networks. Solar eruptions are caused by complex dynamics of sunspots which are often called solar active regions. The goal of this research is to build data infrastructure to characterize the properties of solar active regions from 1970 to now using advanced data from ground-based observatories and satellite missions. The database and associated cyberinfrastructure, jointly to be developed by physicists and computer scientists, will utilize advanced artificial intelligence and machine learning. By using this advanced database, a better understanding of the solar active regions and how they trigger solar eruptions will be achieved. The project has significant education and training components that will involve graduate students and junior researchers. The project will build advanced computer infrastructure to characterize solar active regions (ARs) and apply machine learning tools to predict two most significant forms of solar eruptions: the solar flares and coronal mass ejections (CMEs). The project will address two key science questions: (1) Which parameters and physical processes are most important for the onset of solar eruptions? (2) What is the accuracy of using these parameters to predict solar eruptions? The work will utilize and interface with the infrastructure developed under a previous EarthCube project. It will analyze digitized and digital high-resolution data from the Big Bear Solar Observatory (BBSO) from 1970 to now, current satellite mission data, as well as legacy data for a more comprehensive archive of flares and associated ARs. Dynamic non-potentiality properties of ARs will be derived using advanced imaging and machine learning tools. Deep learning techniques will be used to trace fibril/loop structures in the solar chromosphere and corona. Combining these with coronal field extrapolation will provide novel parameters to describe non-potentiality in ARs. Two new parameters will be derived that may be critically linked to flares and CMEs: flow motions and magnetic helicity injection in flare productive ARs. Based on flare/CME properties and important parameters derived from hosting ARs, deep learning techniques will be further adapted to predict the occurrence and energy range of flares and CMEs.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.

空间天气是一个术语，用于描述太阳系中由太阳表面爆发（如太阳耀斑）引起的环境条件变化。了解和预测太阳爆发对国家安全和经济至关重要，因为众所周知，太阳爆发会对卫星和配电网等关键技术基础设施产生不利影响。太阳爆发是由太阳黑子的复杂动力学引起的，这些黑子通常被称为太阳活动区。这项研究的目标是建立数据基础设施，利用地面观测站和卫星任务的先进数据来表征1970年至今太阳活动区的特性。该数据库和相关的网络基础设施将由物理学家和计算机科学家共同开发，将利用先进的人工智能和机器学习。 通过使用这一先进的数据库，将更好地了解太阳活动区及其如何引发太阳爆发。该项目具有重要的教育和培训内容，将涉及研究生和初级研究人员。该项目将建立先进的计算机基础设施来描述太阳活动区的特征，并应用机器学习工具来预测两种最重要的太阳喷发形式：太阳耀斑和日冕物质抛射。该项目将解决两个关键的科学问题：（1）哪些参数和物理过程对太阳爆发的开始最为重要？(2)使用这些参数来预测太阳爆发的准确性如何？这项工作将利用在以前的一个EarthCube项目下开发的基础设施并与之对接。它将分析从1970年至今来自大熊太阳天文台（BBSO）的数字化和数字化高分辨率数据，当前的卫星使命数据，以及遗留数据，以获得更全面的耀斑和相关AR档案。将使用先进的成像和机器学习工具来推导AR的动态非潜能特性。深度学习技术将用于追踪太阳色球层和日冕中的纤维/环结构。将这些与日冕场外推相结合，将提供新的参数来描述AR中的非潜力。两个新的参数将得出，可能是至关重要的耀斑和日冕物质抛射：流运动和磁螺旋注入耀斑生产AR。根据耀斑/CME的性质和从托管AR中获得的重要参数，深度学习技术将进一步适用于预测耀斑和CME的发生和能量范围。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。

项目成果

An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions

通过确定源活动区域研究太阳黑子群与日冕物质抛射之间的因果关系

• DOI: 10.1093/mnras/stab1816
• 发表时间: 2021
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Raheem, Abd-ur;Cavus, Huseyin;Coban, Gani Caglar;Kinaci, Ahmet Cumhur;Wang, Haimin;Wang, Jason T
• 通讯作者: Wang, Jason T

Machine-learning Approach to Identification of Coronal Holes in Solar Disk Images and Synoptic Maps

识别日盘图像和天气图中日冕洞的机器学习方法

• DOI: 10.3847/1538-4357/abb94d
• 发表时间: 2020
• 期刊: The Astrophysical Journal
• 作者: Illarionov, Egor;Kosovichev, Alexander;Tlatov, Andrey
• 通讯作者: Tlatov, Andrey

Predicting CME arrival time through data integration and ensemble learning

• DOI: 10.3389/fspas.2022.1013345
• 发表时间: 2022-10
• 期刊: Space Weather
• 作者: Khalid A. Alobaid;Yasser Abduallah;J. T. Wang;Haimin Wang;Haodi Jiang;Yan Xu;V. Yurchyshyn;Hongyang Zhang;H. Cavus;J. Jing

Study of Global Photospheric and Chromospheric Flows Using Local Correlation Tracking and Machine Learning Methods I: Methodology and Uncertainty Estimates

• DOI: 10.1007/s11207-023-02158-x
• 发表时间: 2023-05
• 期刊: Solar Physics
• 影响因子: 2.8
• 作者: Qin Li;Yan Xu;M. Verma;C. Denker;Junwei Zhao;Haimin Wang

Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

• DOI: 10.3847/1538-4357/ab8818
• 发表时间: 2020-05
• 期刊: The Astrophysical Journal
• 作者: Hao Liu;Yan Xu;Jiasheng Wang;J. Jing;Chang Liu;J. T. Wang;Haimin Wang