Collaborative Research: ANSWERS: Prediction of Geoeffective Solar Eruptions, Geomagnetic Indices, and Thermospheric Density Using Machine Learning Methods

合作研究：答案：使用机器学习方法预测地球有效太阳喷发、地磁指数和热层密度

• 批准号: 2149750
• 负责人: Katherine Herbert
• 金额: $ 4.49万
• 依托单位: Montclair State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149750&HistoricalAwards=false
• 关键词: Collaborative; Research; ANSWERS; Prediction; Geoeffective

Understanding and predicting eruptions on the Sun and their terrestrial impacts are a research as well as strategic national priority, as such space weather affects our electronic communication, electric power supply, satellite infrastructure, national defense, and more. This project is a collaboration among Rutgers University, New Jersey Institute of Technology, West Virginia University, and Montclair State University that will improve our ability to predict several linked space weather components: geoeffective solar eruptions, the global magnetic response of Earth to these eruptions, as well as variation of neutral density in the Earth’s thermosphere and its effect on satellite drag. The work covers many aspects of geospace science, solar physics, and data science including machine learning. The innovative machine learning tools developed from the project will be applicable for analyzing disparate data sets in astronomy and other areas of science. Faculty members, early career researchers including a postdoctoral fellow and graduate students will collaborate on the project, creating a multidisciplinary training program for future generations of scientists. The project will emphasize diversity and the participation of underrepresented minorities through both the research efforts and education activities such as K-12 teacher workshops.The two key science questions are: What are the physical mechanisms for the onset of geoeffective solar eruptions? And what are the effects of solar eruptions on neutral density in the thermosphere? Specifically, the project will create synthetic vector magnetograms using ground- and space-based data for solar cycles 23 and 24; develop machine learning (ML) tools to predict solar flares and associated geoeffective coronal mass ejections (CMEs) based on magnetogram parameters; predict geomagnetic indices from derived magnetic properties of solar active regions and CMEs, solar wind parameters and solar images; and predict neutral density in the thermosphere using ML approaches that integrate satellite data, observed and predicted geomagnetic indices, and empirical neutral density models. Most of the funding will be used to support three graduate students (one at WVU and two at NJIT) and a postdoc at Rutgers. K-12 teacher workshops will be organized by Montclair State University. ANSWERS projects advance the nation’s STEM expertise and societal resilience to space weather hazards by filling key knowledge gaps regarding the coupled Sun-Earth system.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.

理解和预测太阳及其陆地影响的爆发既是一项研究，也是战略性的国家优先事项，因为这种太空的天气会影响我们的电子通信，电力供应，卫星基础设施，国防等等。该项目是罗格斯大学，新泽西大学，西弗吉尼亚大学和蒙特克莱州立大学之间的合作，该项目将提高我们预测几种连接的太空天气组成部分的能力：地理效能太阳喷发，地球对这些爆发的全球磁反应以及地球中性密度的变化以及对卫星效果的变化。这项工作涵盖了地理水平科学，太阳能物理学和数据科学的许多方面，包括机器学习。从该项目开发的创新机器学习工具将适用于天文学和其他科学领域的分析数据集。教师，早期职业研究人员在内，包括博士后研究员和研究生将在该项目上合作，为子孙后代创建一个多学科培训计划。该项目将通过研究工作和教育活动（例如K-12教师研讨会）强调多样性和代表性不足的少数群体的参与。两个关键的科学问题是：地理效果太阳喷发发作的物理机制是什么？太阳喷发对热层中性密度有什么影响？具体而言，该项目将使用太阳能循环23和24的地面和空间数据创建合成矢量磁图；开发机器学习（ML）工具，以预测基于磁图参数的太阳耀斑和相关的地理冠状质量弹出（CME）；从太阳能活动区和CME，太阳风参数和太阳图像的衍生磁性特性中预测地磁指数；并使用集成卫星数据，观察和预测地磁指数和经验中性密度模型的ML方法预测热圈中的中性密度。大多数资金将用于支持三名研究生（一名在WVU，两名在NJIT）和Rutgers的博士后。 K-12教师讲习班将由蒙特克莱尔州立大学组织。答案项目通过填补有关耦合太阳 - 地球系统的关键知识差距来提高国家的STEM专业知识和社会弹性对太空天气危害。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响来评估NSF的法定任务。