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.

太阳耀斑是发生的最有影响力的太阳喷发活动之一。它们是由于磁重新连接引起的，高度波动的磁场崩溃到较低的能量状态，将能量释放到太空中。该项目通过使用机器学习技术来预测太阳耀斑的预测太阳能观察。更广泛的影响包括公众对犹他州所有教育水平的学生的影响，从中学到大学一级，通过在犹他州公共广播中组织年度太空天气小组讨论。每年夏天，美国土著学生的研究经验将得到资助。此外，PI是一位早期的职业女科学家，将为她的应用数据挖掘课程开发新的应用实验室组件。这项研究有可能通过减少与太空天气事件的国防和太空探索任务相关的潜在风险来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域磁场参数已常规用于预测太阳耀斑的发生。尽管最近在太阳火光预测方面取得了进步，但仍有重大障碍可以有效地结合跨越多个太阳能循环的不同太空工具的观测值，以训练强大而无偏的太阳耀斑模型。该项目是一项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能循环中极端太阳耀斑的驱动因素，评估活性区域对太阳能瞬态事件的影响，并将学习的知识转移到其他模型中。为了实现视力，该项目将基于板上NASA的三个磁力仪太阳能动力学观测站，太阳能和地球层观测值和Heliosperic Perservatory和Heliosperic observatory和HINODE（推力1），将开发一个高空间分辨率的活动区域矢量磁力图数据集，该数据集跨越了两个太阳能循环。新的高质量和高分辨率磁场图将允许研究小型活动区域的物理特征，这些特征从未在太阳耀斑预测的背景下进行检查。该项目将生成全面的磁场参数多变量时间序列（MVTS）数据集，可用于数据科学和太空天气群落，用于建模各种太阳现象（推力2）。最后，该项目将建立一个准确，健壮的太阳耀斑预测模型，并使用学习的预测模式来初始化其他太阳事件预测模型（推力3）。这项职业建议的最终目标是利用天体物理学领域应用的ML方法的跨场来提高我们对驱动不同类型的太阳能耀斑的活性区域的物理属性的理解，并使科学家能够进行比较，可重复的，可重复的和数据驱动的对Solar Flare事件预测的研究。 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 free 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 We were deemed honestly of support through使用基金会的智力优点和更广泛的影响评估标准进行评估。

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

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

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

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