Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques

TL;DR

This paper introduces automated clustering-based methods to detect magnetic reconnection events in kinetic plasma simulations, demonstrating competitive performance with traditional proxy-based techniques.

Contribution

It develops and tests unsupervised machine learning algorithms, specifically K-means and DBSCAN, for identifying reconnection events in large simulation datasets, proposing an optimal aspect ratio threshold.

Findings

Unsupervised clustering techniques perform competitively with proxy-based methods.

An optimal aspect ratio of 18 improves detection accuracy.

The methods enable automated analysis of large simulation datasets.

Abstract

Kinetic turbulence in magnetized space plasmas has been extensively studied via in situ observations, numerical simulations and theoretical models. In this context, a key point concerns the formation of coherent current structures and their disruption through magnetic reconnection. We present automatic techniques aimed at detecting reconnection events in large data set of numerical simulations. We make use of clustering techniques known as K-means and DBscan (usually referred in literature as unsupervised machine learning approaches), and other methods based on thresholds of standard reconnection proxies. All our techniques use also a threshold on the aspect ratio of the regions selected. We test the performance of our algorithms. We propose an optimal aspect ratio to be used in the automated machine learning algorithm: AR=18. The performance of the unsupervised approach results to be strongly competitive with respect to those of other methods based on thresholds of standard reconnection proxies.