Unsupervised classification of simulated magnetospheric regions

Abstract

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.

Unsupervised classification of fully kinetic simulations of plasmoid instability using self-organizing maps (SOMs)

“…The SOMs are at the core of the clustering procedure we use in this work, which has already been used (with minimal variation) and described in Amaya et al (2020) and Innocenti et al (2021). We briefly describe the method here for the reader's convenience:…”

“…In Amaya et al (2020), it has been applied to 14 years of Advanced Composition Explorer (ACE, Stone et al 1998) solar wind measurements. In Innocenti et al (2021), it has been used to cluster simulated data, and specifically data from a global magnetospheric simulation. The code used there was the magneto-hydro-dynamic (MHD)-based code OpenGGCM-CTIM-RCM (Raeder 2003), which targets large-scale processes originating from the interaction of the solar wind with the magnetosphere-ionosphere-thermosphere system.…”

“…One rather fundamental question left open in Innocenti et al (2021) was whether a similar clustering procedure would produce equally meaningful results when applied to smaller-scale processes, kinetic in nature. The MHD simulations intend to reproduce plasma behaviour at scales large enough that certain assumptions can be considered satisfied.…”

“…These assumptions are obviously not respected in most heliospheric plasmas, especially at the very small and fast scales sampled by recent magnetospheric and solar wind missions, such as the above-mentioned MMS, PSP and Solar Orbiter. Hence, the need arises to verify if the clustering procedure described in Innocenti et al (2021) is robust to using simulation approaches, such as PIC methods, that deliver results directly comparable to observations (Innocenti et al 2016). This work intends to address this question.…”

“…On the other hand, this analysis constitutes a necessary first step to validate the method before applying it to spacecraft observations. We apply the clustering procedure from Innocenti et al (2021) to a fully kinetic PIC simulation of the plasmoid instability. The plasmoid instability is a fast instability that breaks down current sheets into multiple magnetic islands, which later undergo nonlinear evolution.…”