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Abstract
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 multistep 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.
Highlights
The growing amount of data produced by measurements and simulations of different aspects of the heliospheric environment has made it fertile ground for applications rooted in artificial intelligence, AI, and machine learning, ML (Bishop, 2006; Goodfellow et al, 2016)
Further information of interest is provided in Appendix A, where we report on a manual exploration of the self-organizing maps (SOMs) hyperparameter space, and in Appendix B, where we assess how robust our classification method is by changing the number of k-means clusters used to classify the SOM nodes
For our unsupervised classification experiments, we initially focus on a single temporal snapshot of the OpenGGCMCTIM-RCM simulation, t0 + 210 min
Summary
The growing amount of data produced by measurements and simulations of different aspects of the heliospheric environment has made it fertile ground for applications rooted in artificial intelligence, AI, and machine learning, ML (Bishop, 2006; Goodfellow et al, 2016). Much of the AI/ML effort in space physics is directed at the Sun itself, either in the form of classification of solar images (Armstrong and Fletcher, 2019; Love et al, 2020) or for the forecast of transient solar events (see Bobra and Couvidat, 2015; Nishizuka et al, 2017; Florios et al, 2018, and references therein) This is not surprising, since the Sun is the driver of the heliospheric system and the ultimate cause of space weather (Bothmer and Daglis, 2007). Solar imaging is one of the fields in science where data are being produced at an increasingly faster rate (see Fig. 1 in Lapenta et al, 2020)
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