Unsupervised classification of simulated magnetospheric regions

Abstract

In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational 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 dimensionality of the data is reduced with Principal Component Analysis before classification. 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. We validate our classification results by plotting the classified data in the simulated space and by comparing with K-means classification. 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, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

References in corpus (12)

• A Tutorial on Principal Component Analysis
• The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Magnification Control in Self-Organizing Maps and Neural Gas
• Automatic classification of plasma regions in near-Earth space with supervised machine learning: application to Magnetospheric Multi Scale 2016-2019 observations
• Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
• Using dimensionality reduction and clustering techniques to classify space plasma regimes
• Visualizing and Interpreting Unsupervised Solar Wind Classifications
• Analysing AIA Flare Observations using Convolutional Neural Networks
• Automatic Region Identification over the MMS Orbit by Partitioning n-T space

Cited by in corpus (2)

• A database of MMS bow shock crossings compiled using machine learning
• Clustering Wind data at 1 AU to contextualize magnetic reconnection in the solar wind