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.

太空天气是一个用来描述由太阳表面（例如太阳耀斑）爆发引起的太阳系中环境条件变化的术语。对太阳喷发的理解和预测对于国家安全和经济至关重要，因为众所周知，它们对卫星和电源分销网络等关键技术基础设施产生不利影响。太阳喷发是由日晒的复杂动力学引起的，黑子通常称为太阳活动区域。这项研究的目的是构建数据基础架构，以使用基于地面的观测站和卫星任务的高级数据来表征从1970年到现在的太阳能活动区域的性质。由物理学家和计算机科学家共同开发的数据库和相关的网络基础设施将利用先进的人工智能和机器学习。 通过使用此高级数据库，将更好地了解太阳能活动区以及它们如何触发太阳喷发。该项目具有重要的教育和培训组成部分，其中将涉及研究生和初级研究人员。该项目将建立高级计算机基础架构以表征太阳能活动区域（ARS）并应用机器学习工具以预测两种最重要的太阳喷发形式：太阳耀斑和冠状质量弹出（CMES）。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于太阳喷发的发作最为重要？ （2）使用这些参数预测太阳喷发的准确性是什么？这项工作将利用并与以前的EarthCube项目开发的基础架构进行交互。它将从1970年到现在分析来自大熊太阳能天文台（BBSO）的数字化和数字高分辨率数据，当前的卫星任务数据以及旧数据，以提供更全面的耀斑和相关ARS档案。 ARS的动态非电位属性将使用高级成像和机器学习工具得出。深度学习技术将用于追踪太阳能球和电晕中的原纤维/环结构。将它们与冠状场外推结合将提供新的参数，以描述ARS中的非电位性。将得出两个新参数，这些参数可能与耀斑和CME密切相关：Flare生产AR中的流动运动和磁性螺旋性注射。基于托管AR的耀斑/CME特性和重要参数，将进一步适应深度学习技术，以预测耀斑和CMES的出现和能量范围。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估Criteria criteria criteria criteria criteria criteria的评估。

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