Unsupervised classification of simulated magnetospheric regions

TL;DR

This paper introduces an unsupervised classification method using Self Organizing Maps to identify magnetospheric regions from simulation data, aiding automatic detection of regions of interest in space weather studies.

Contribution

It presents a novel application of SOMs combined with PCA for classifying magnetospheric regions based solely on local plasma properties, validated against K-means clustering.

Findings

Successfully classified magnetospheric regions in simulation data

Identified key features that improve classification effectiveness

Validated results with K-means and feature map analysis

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.