The growing amount of data produced by simulations and observations of space physics processes encourages the use of methods rooted in Machine Learning for data analysis and physical discovery. We apply a clustering method based on Self-Organizing Maps (SOM) to fully kinetic simulations of plasmoid instability, with the aim of assessing its suitability as a reliable analysis tool for both simulated and observed data. We obtain clusters that map well, a posteriori, to our knowledge of the process: the clusters clearly identify the inflow region, the inner plasmoid region, the separatrices, and regions associated with plasmoid merging. SOM-specific analysis tools, such as feature maps and Unified Distance Matrix, provide one with valuable insights into both the physics at work and specific spatial regions of interest. The method appears as a promising option for the analysis of data, both from simulations and from observations, and could also potentially be used to trigger the switch to different simulation models or resolution in coupled codes for space simulations.

翻译：空间物理过程模拟与观测产生数据的日益增长，推动了基于机器学习的方法在数据分析和物理发现中的应用。我们采用基于自组织映射(SOM)的聚类方法对等离子体团不稳定性的全动力学模拟进行分析，旨在评估该方法作为模拟与观测数据可靠分析工具的适用性。获得的聚类结果在事后阶段能够很好地映射我们对这一过程的认知：这些聚类清晰识别出流入区域、内部等离子体团区域、分离面以及与等离子体团合并相关的区域。特征映射和统一距离矩阵等SOM专用分析工具，为理解物理机制和特定空间感兴趣区域提供了宝贵的见解。该方法作为模拟与观测数据分析的一种有前景的选择，同时也可能触发耦合空间模拟代码中不同模拟模型或分辨率的切换。