Leveraging the mathematics of shape for solar magnetic eruption prediction
Leveraging the mathematics of shape for solar magnetic eruption prediction
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DOI：
10.1051/swsc/2020014
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发表时间：
2020-03
影响因子：
3.3
通讯作者：
V. Deshmukh;T. Berger;E. Bradley;J. Meiss
中科院分区：
文献类型：
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作者：
V. Deshmukh;T. Berger;E. Bradley;J. Meiss
目前对太阳爆发的业务预测是由人类专家使用基于形状的定性分类系统和有关耀斑频率的历史数据相结合进行的。在过去的十年中，人们对机器学习（ML）耀斑预测方法产生了极大的兴趣，以从训练集中提取潜在的模式-例如一组太阳磁图图像，每个图像的特征都来自磁场，并标记它是否是喷发前兆。这些模式，通过各种方法（神经网络，支持向量机等）捕获，然后可以用于对新图像进行分类。任何ML方法的一个主要挑战是数据的特征化：预处理原始图像以提取更高级别的属性，例如磁场的特征，这可以简化这些方法的训练和使用。从手头任务的角度来看，关键是选择信息丰富的功能。迄今为止，大多数基于ML的太阳喷发方法都使用基于物理的磁场和电场特征，例如总无符号磁通量、场梯度、垂直电流密度等。我们扩展了相关的特征集，以包括纯粹基于2D磁图图像的几何形状和拓扑结构的磁场特征，并表明这提高了基于神经网络的耀斑预测方法的预测精度。
Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal of interest in crafting machine-learning (ML) flare-prediction methods to extract underlying patterns from a training set – e.g. a set of solar magnetogram images, each characterized by features derived from the magnetic field and labeled as to whether it was an eruption precursor. These patterns, captured by various methods (neural nets, support vector machines, etc.), can then be used to classify new images. A major challenge with any ML method is thefeaturizationof the data: pre-processing the raw images to extract higher-level properties, such as characteristics of the magnetic field, that can streamline the training and use of these methods. It is key to choose features that are informative, from the standpoint of the task at hand. To date, the majority of ML-based solar eruption methods have used physics-based magnetic and electric field features such as the total unsigned magnetic flux, the gradients of the fields, the vertical current density, etc. In this paper, we extend the relevant feature set to include characteristics of the magnetic field that are based purely on the geometry and topology of 2D magnetogram images and show that this improves the prediction accuracy of a neural-net based flare-prediction method.