Abstract. In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or opera- tional interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

中文翻译：

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模拟磁层区域的无监督分类

摘要。在磁层任务中，突发模式数据采样应在存在科学或操作兴趣的过程中触发。我们提出了一种磁层区域的无监督分类方法，它可以构成自动识别感兴趣的磁层过程的多步方法的第一步。我们的方法基于自组织图 (SOM)，我们在使用 OpenGGCM-CTIM-RCM 代码获得的全球磁层模拟数据点上对其进行初步测试。分类完全依赖于本地选定数据点的等离子体特性，没有关于它们的邻域或它们的时间演变的信息。我们将 SOM 节点分类为自动选择数量的类，并获得映射到明确定义的磁层区域的集群。为了结果的可解释性，我们检查 SOM 特征图（磁层变量在分类上下文中称为特征），并使用它们来解锁有关集群的信息。我们使用不同的特征集重复分类实验，并深入了解哪些磁层变量为无监督分类提供了更有效的特征。