CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning

职业：使用多航天器数据和机器学习的基于端对端活动区域的日光层预报系统

• 批准号: 2240022
• 负责人: Soukaina Filali Boubrahimi
• 金额: $ 69.2万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240022&HistoricalAwards=false
• 关键词: CAREER; End; Active; Region; based

Solar flares are one of the most impactful solar eruptive activities that occur. They are due to magnetic reconnection, where highly fluctuating magnetic fields collapse to a lower energy state releasing energy into space. This project focuses on prediction of solar flares through using machine learning techniques on solar observations. The broader impacts include public outreach to impact students at all educational levels in Utah from middle-school to college level by organizing a yearly space-weather panel discussion on Utah Public Radio. Indigenous American student research experiences will be funded every summer. In addition, the PI, an early career woman scientist, will develop a new applied laboratory component to her applied data mining classes. The research has the potential to increase national security and US competitiveness by lessening the potential risk related to defense and space exploration missions from space weather events. Active region magnetic field parameters, extracted from solar photospheric vector magnetograms, have been routinely used to predict solar flare occurrences. Despite recent advancements in solar flare prediction, there are significant barriers to efficiently combine different space-borne instruments’ observations spanning multiple solar cycles, to train robust and unbiased solar flare models. This project is a five-year research program that aims at leveraging state-of-the-art machine learning models to discover the driving factors of extreme solar flares in different solar cycles, assess the impact of active regions’ properties on solar transient events, and transfer the learned knowledge to other models of the heliosphere. To achieve the vision, the project will develop a high-spatial resolution active regions vector magnetogram dataset, that spans two solar cycles, based on three magnetographs on-board NASA’s Solar Dynamics Observatory, Solar and Heliospheric Observatory and Hinode (Thrust 1). The new high- quality and high-resolution magnetic field maps will allow the study of small-scale active regions’ physical characteristics that were never examined in the context of solar flare prediction. The project will generate comprehensive magnetic field parameters multivariate time series (MVTS) dataset useful to both Data Science and Space Weather communities for modeling various solar phenomena (Thrust 2). Finally, the project will build an accurate and robust solar flare prediction model and use the learned predictive patterns to initialize other solar events predictive models (Thrust 3). The end goal of this CAREER proposal, is to leverage the cross-field of applied ML methods in the field of astrophysics to improve our understanding of the physical attributes of active regions that drive different types of solar flares, and enable scientists to perform comparative, reproducible, and data-driven studies on the prediction of solar flare events. One of the by-products of this research will be an unprecedented comprehensive solar flare catalog supplemented with parent active regions’ magnetic field parameters’ multivariate time series data that will be freely available through Application Programming Interface (API) for its wide potential usage (e.g., conduct statistical studies, train ML-based and physics-based models).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.

太阳耀斑是最具影响力的太阳爆发活动之一。它们是由于磁场重联，其中高度波动的磁场坍缩到较低的能量状态，将能量释放到太空中。该项目的重点是通过在太阳观测中使用机器学习技术来预测太阳耀斑。更广泛的影响包括通过在犹他州公共广播电台组织年度空间气象小组讨论，对犹他州从中学到大学的所有教育水平的学生进行公众宣传。每年夏天都会资助美国原住民学生的研究经验。此外，PI，一个早期的职业女性科学家，将开发一个新的应用实验室组成部分，她的应用数据挖掘类。这项研究有可能通过减少太空天气事件对国防和太空探索任务的潜在风险，提高国家安全和美国的竞争力。从太阳光球矢量磁图中提取的活动区磁场参数已被常规地用于预测太阳耀斑的发生。尽管最近在太阳耀斑预测方面取得了进展，但要有效地将跨越多个太阳活动周期的不同空间仪器的观测结果联合收割机结合起来，以训练稳健和无偏见的太阳耀斑模型，仍然存在重大障碍。该项目是一个为期五年的研究计划，旨在利用最先进的机器学习模型来发现不同太阳周期中极端太阳耀斑的驱动因素，评估活动区域属性对太阳瞬态事件的影响，并将学到的知识转移到日光层的其他模型。为实现这一愿景，该项目将根据美国航天局太阳动力学观测台、太阳和日光层观测台和日出（推力1）上的三个磁象仪，开发一个跨越两个太阳周期的高空间分辨率活动区矢量磁象数据集。新的高质量和高分辨率磁场图将使人们能够研究小规模活动区域的物理特征，这些特征在太阳耀斑预测中从未得到过研究。该项目将生成对数据科学和空间气象界有用的综合磁场参数多变量时间序列数据集，用于模拟各种太阳现象（推力2）。最后，该项目将建立一个准确和强大的太阳耀斑预测模型，并使用学习到的预测模式来初始化其他太阳事件预测模型（推力3）。该CAREER提案的最终目标是利用天体物理学领域应用ML方法的跨领域，以提高我们对驱动不同类型太阳耀斑的活动区域物理属性的理解，并使科学家能够对太阳耀斑事件的预测进行比较，可重复和数据驱动的研究。这项研究的副产品之一将是一个前所未有的全面的太阳耀斑目录，补充了母活动区的“磁场参数”多变量时间序列数据，这些数据将通过应用程序编程接口（API）免费提供，以获得其广泛的潜在用途（例如，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

Motif Alignment for Time Series Data Augmentation

时间序列数据增强的基序对齐

• 发表时间: 2023
• 期刊: DAWAK
• 作者: Bahri, Omar;Li, Peiyu;Filali Boubrahimi, Soukaına;Hamdi, Shah Muhammad
• 通讯作者: Hamdi, Shah Muhammad

Attention-based Counterfactual Explanation for Multivariate Time Series

基于注意力的多元时间序列反事实解释

• 发表时间: 2023
• 期刊: Lecture notes in computer science
• 作者: Li, Peiyu;Bahri, Omar;Filali Boubrahimi, Soukaına;Hamdi, Shah Muhammad
• 通讯作者: Hamdi, Shah Muhammad

CELS: Counterfactual Explanations for Time Series Data via Learned Saliency Maps

• DOI: 10.1109/bigdata59044.2023.10386229
• 发表时间: 2023-12
• 期刊: 2023 IEEE International Conference on Big Data (BigData)
• 作者: Peiyu Li;O. Bahri;S. F. Boubrahimi;S. M. Hamdi

Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation

通过多元时间序列数据增强改进太阳能粒子事件预测

• DOI: 10.3847/1538-4365/ad1de0
• 发表时间: 2024
• 期刊: The Astrophysical Journal Supplement Series
• 作者: Hosseinzadeh, Pouya;Filali Boubrahimi, Soukaina;Hamdi, Shah Muhammad
• 通讯作者: Hamdi, Shah Muhammad

Multiloss-Based Optimization for Time Series Data Augmentation

基于多重损失的时间序列数据增强优化

• DOI: 10.1109/bigdata59044.2023.10386614
• 发表时间: 2023
• 期刊: IEEE
• 作者: Bahri, Omar;Li, Peiyu;Boubrahimi, Soukaïna Filali;Hamdi, Shah Muhammad
• 通讯作者: Hamdi, Shah Muhammad