NSWP: Automated Monitoring and Forecasting of Space Weather using Artificial Intelligence Techniques

提议的团队将使用模式识别技术来预测太阳耀斑的发生，开发一种称为支持向量机（SVM）的新工具。提议者打算开发工具来检测太阳上出现的新磁通量，使用圆谐波分量分解作为人工智能分类器的过滤器。这项技术将表征新的通量出现，并建立活跃地区成为耀斑生产的概率。他们将实施太阳耀斑检测和表征算法，包括使用SVM的分类方案，活动区域生长和边缘增强技术。该算法将检测耀斑带分离，并帮助确定磁场重联区域的电流。PI还将使用表征程序研究大量CME，以建立CME速度和磁场重联率之间的关系。这将允许基于磁场重联的真实的实时监测来预测CME动力学。这一努力将加强我们对影响太阳活动的过程以及由此产生的太阳效应通过太阳风传播到地球的理解和预测。 这项工作本质上是跨学科的，涉及尖端的太阳物理学和计算机科学研究。这里开发的技术也有潜在的效用，医学成像，地面天气预报，和模式识别相关的军事应用的移动目标。这项建议的教育和培训部分涉及一名新毕业的博士后研究员和一名研究生的支助。

The proposing team will use pattern recognition techniques to predict the occurrence of solar flares, developing a new tool known as the Support Vector Machine (SVM). The proposers intend to develop tools to detect new magnetic flux emergence on the Sun, using circular harmonic component decomposition as a filter for an artificial intelligence classifier. This technique will characterize new flux emergence and establish the probabilities for active regions to become flare productive. They will implement a solar flare detection and characterization algorithm, including a classification scheme using the SVM, active region growth, and edge enhancement techniques. The algorithm will detect flare ribbon separations and help determine the electric currents in magnetic reconnection regions. The PI will also study a large number of CMEs using a characterization routine to establish the relationship between CME speed and magnetic reconnection rate. This will allow the prediction of CME kinetics based on real- time monitoring of magnetic reconnection. This effort will enhance our understanding and prediction of processes affecting solar activity and the propagation of resulting solar effects to the Earth via the solar wind. The work is inherently interdisciplinary, involving cutting-edge solar physics and computer science research. The techniques developed here also have potential utility for medical imaging, terrestrial weather forecasting, and pattern recognition for moving targets relevant to military applications. This proposal's education and training component involves the support of a newly graduated post-doctoral researcher and a graduate student.