SHINE: Prediction of Coronal Mass Ejections and Interplanetary Magnetic Fields Using Advanced Artificial Intelligence Techniques

SHINE：利用先进人工智能技术预测日冕物质抛射和行星际磁场

• 批准号: 2300341
• 负责人: Vasyl Yurchyshyn
• 金额: $ 57.32万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300341&HistoricalAwards=false
• 关键词: SHINE; Prediction; Coronal; Mass; Ejections

Understanding and predicting violent solar eruptions and their effect on Earth is a strategic national priority, as it affects the daily life of human beings, including communication, transportation, power supply systems, national defense, space travel, and more. Due to increasing spatial and temporal resolution of solar instrumentation, researchers are facing tremendous challenges in analyzing massive amounts of space weather data, especially for the operational near real-time utilization. This interdisciplinary project advanced artificial intelligence (AI) based tools to forecast geoeffective coronal mass ejections (CMEs). A graduate student will be supported by this project and undergraduate students will be mentored. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences and by the Space Weather program.This project supports fundamental research on advanced AI to forecast CMEs and their potential to cause geomagnetic storms near 1 AU. The objectives are to (i) employ AI to predict whether a solar active region will produce a CME and estimate its transit time, mass, and kinetic energy, and (ii) predict the orientation of magnetic clouds (MCs) near 1 AU, based on real time solar observations. Data will be used by graph neural networks (GNNs) and ensemble learning methods to combine the GNNs with other conventional AI techniques to predict orientations of MCs. The project utilizes data from NASA, NOAA, and NSF observatories, including the NSF-funded Global H-alpha Network and Big Bear Solar Observatory. The study will also utilize existing measurements and model results, which will be augmented with additional measurements derived from global coronal field maps as well as non-linear force-free field modeling.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

了解和预测强烈的太阳爆发及其对地球的影响是国家的战略重点，因为它影响着人类的日常生活，包括通信、交通、电力供应系统、国防、太空旅行等。随着太阳仪器空间和时间分辨率的不断提高，研究人员在分析海量空间气象数据方面面临着巨大的挑战，特别是在近实时的操作利用方面。这个跨学科项目先进的人工智能（AI）为基础的工具来预测地球有效的日冕物质抛射（cme）。该项目将支持一名研究生，并指导本科生。该项目由地球科学理事会和空间天气计划共同资助，以支持地球科学领域的人工智能/机器学习进展。该项目支持先进人工智能的基础研究，以预测日冕物质抛射及其在1 AU附近引起地磁风暴的可能性。目标是(i)利用人工智能预测太阳活动区域是否会产生日冕物质抛射，并估计其过境时间、质量和动能，以及（ii）基于实时太阳观测预测1 AU附近磁云（mc）的方向。数据将被图神经网络（gnn）和集成学习方法使用，将gnn与其他传统的人工智能技术相结合，以预测mc的方向。该项目利用了来自NASA、NOAA和NSF天文台的数据，包括NSF资助的全球h - α网络和大熊太阳天文台。该研究还将利用现有的测量和模型结果，并将通过全球日冕场图和非线性无力场建模获得额外的测量结果。该项目由地球科学理事会共同资助，旨在支持人工智能/机器学习在地球科学领域的发展。该奖项反映了美国国家科学基金会的法定使命，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

Improving spatial resolution of sunspot HMI images using conditional generative adversarial networks

使用条件生成对抗网络提高太阳黑子 HMI 图像的空间分辨率

• DOI: 10.1063/5.0181507
• 发表时间: 2023
• 期刊: American Institute of Physics Conference Series
• 作者: Prianto, Agus;Wibowo, Ridlo Wahyudi;Putri, Gerhana Puannandra;Huda, Ibnu Nurul;Yurchyshyn, Vasyl;Malasan, Hakim Luthfi
• 通讯作者: Malasan, Hakim Luthfi

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

• DOI: 10.1029/2023sw003824
• 发表时间: 2024-02
• 期刊: Space Weather
• 作者: Yasser Abduallah;Khalid A. Alobaid;Jason T. L. Wang;Haimin Wang;V. Jordanova;Vasyl Yurchyshyn;Huseyin Cavus;Ju Jing

Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models

通过融合多个深度学习模型来估计日冕物质抛射质量和动能

• DOI: 10.3847/2041-8213/ad0c4a
• 发表时间: 2023
• 期刊: The Astrophysical Journal Letters
• 作者: Alobaid, Khalid A.;Abduallah, Yasser;Wang, Jason T. L.;Wang, Haimin;Fan, Shen;Li, Jialiang;Cavus, Huseyin;Yurchyshyn, Vasyl
• 通讯作者: Yurchyshyn, Vasyl

Relationships Between Physical Parameters of Umbral Dots Measured for 12 Sunspot Umbras with the Goode Solar Telescope

• DOI: 10.1007/s11207-023-02198-3
• 发表时间: 2023-09
• 期刊: Solar Physics
• 影响因子: 2.8
• 作者: M. A. Calisir;H. T. Yazici;A. Kilçik;V. Yurchyshyn

Magnetic Relaxation Seen in a Rapidly Evolving Light Bridge in a Sunspot

• DOI: 10.3847/1538-4357/ad1ab0
• 发表时间: 2024-02
• 期刊: The Astrophysical Journal
• 作者: D. Song;E. Lim;J. Chae;Y. Kim;Yukio Katsukawa;V. Yurchyshyn