Elements: Spatiotemporal Analysis of Magnetic Polarity Inversion Lines (STEAMPIL)

元素：磁极性反转线的时空分析 (STEAMPIL)

• 批准号: 2104004
• 负责人: Berkay Aydin
• 金额: $ 59.99万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104004&HistoricalAwards=false
• 关键词: Elements; Spatiotemporal; Analysis; Magnetic; Polarity

Extreme space weather events such as solar flares, coronal mass ejections, energetic proton events and geomagnetic storms can cause massive disruptions in many technologically complex systems, including radio communications, telecommunication and navigation satellites, electrical power systems, or space and even commercial airline flights. This project builds a detection and analysis cyberinfrastructure, and investigates one of the most distinctive features in the solar atmosphere – magnetic polarity inversion lines, which are hotspots of the most intense eruptive activity. Analyzing these features enables solar physicists to advance understanding of extreme space weather events and provide needed predictive capabilities for space weather forecasters.This project creates an innovative and sustainable software infrastructure to detect, characterize and analyze polarity inversion lines. The first step toward that objective is the identification of polarity inversion lines, and quantitative characterization of these multi-faceted features through image descriptors. In subsequent stages, this project analyzes the time series of these features and descriptors using advanced machine learning and data mining techniques, specifically for improving space weather forecasting capabilities. This includes analyzing the spatiotemporal patterns of emergence and disappearance for polarity inversion lines, selecting and understanding important shape characteristics of these lines pertinent to solar eruptive activity, and creating a prototype eruption forecasting system with discovered precursors. Automatically identifying and analyzing polarity inversion lines has several direct benefits: physically understanding solar magnetic shear layers and the transition from typical non-eruptive active region states to intense, eruptive ones; making contributions to forecasting of solar eruptions; and generating descriptors and measures that can be useful to the study of shear layers in nature.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Solar Terrestrial Physics Program and the Division of Integrative and Collaborative Education and Research within the NSF Directorate for 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.

太阳耀斑、日冕物质抛射、高能质子事件和地磁风暴等极端太空天气事件可能会对许多技术复杂的系统造成巨大破坏，包括无线电通信、电信和导航卫星、电力系统、太空甚至商业航班。 该项目建立了一个探测和分析网络基础设施，并研究了太阳大气中最显着的特征之一——磁极性反转线，这是最强烈的喷发活动的热点。分析这些特征使太阳物理学家能够加深对极端空间天气事件的理解，并为空间天气预报员提供所需的预测能力。该项目创建了一个创新且可持续的软件基础设施，用于检测、表征和分析极性反转线。实现该目标的第一步是识别极性反转线，并通过图像描述符对这些多方面特征进行定量表征。在后续阶段，该项目使用先进的机器学习和数据挖掘技术来分析这些特征和描述符的时间序列，特别是为了提高空间天气预报能力。这包括分析极性反转线出现和消失的时空模式，选择和理解与太阳喷发活动相关的这些线的重要形状特征，以及利用已发现的前兆创建原型喷发预报系统。自动识别和分析极性反转线有几个直接的好处：从物理上理解太阳磁剪切层以及从典型的非喷发活动区域状态到强烈的喷发区域状态的转变；为太阳喷发的预测做出贡献；并生成可用于研究自然界剪切层的描述符和测量方法。该奖项由先进网络基础设施办公室颁发，并得到日地物理计划和 NSF 地球科学理事会综合协作教育与研究部的共同支持。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的评估进行评估，认为值得支持。 影响审查标准。

项目成果

Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods

• DOI: 10.1007/978-3-031-43430-3_5
• 发表时间: 2023-07
• 期刊: Academic Journal of Computing & Information Science
• 作者: Chetraj Pandey;R. Angryk;Berkay Aydin

Flare to CME Association Integration

Flare 与 CME 协会整合

• DOI: 10.7910/dvn/wsey4t
• 发表时间: 2022
• 期刊: Harvard Dataverse
• 作者: Ji, Anli;Aydin, Berkay
• 通讯作者: Aydin, Berkay

Beyond Traditional Flare Forecasting: A Data-driven Labeling Approach for High-fidelity Predictions

超越传统的耀斑预测：用于高保真预测的数据驱动标记方法

• 发表时间: 2023
• 期刊: The 25th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2023
• 作者: Hong, Jinsu;Ji, Anli;Pandey, Chetraj;Aydin, Berkay
• 通讯作者: Aydin, Berkay

A Systematic Magnetic Polarity Inversion Lines Dataset from SDO and HMI Magnetograms

来自 SDO 和 HMI 磁图的系统磁极性反演线数据集

• DOI: 10.7910/dvn/bkp1rh
• 发表时间: 2022
• 期刊: Harvard Dataverse
• 作者: Ji, Anli;Cai, Xumin;Khasayeva, Nigar;Georgoulis, Manolis;Martens, Petrus;Angryk, Rafal;Aydin, Berkay
• 通讯作者: Aydin, Berkay

Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting

• DOI: 10.3389/fspas.2022.897301
• 发表时间: 2022-08
• 期刊: The Astrophysical Journal
• 作者: Chetraj Pandey;Anli Ji;R. Angryk;M. Georgoulis;Berkay Aydin