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Kinetic turbulence in magnetized space plasmas has been extensively studiedvia in situ observations, numerical simulations and theoretical models. In thiscontext, a key point concerns the formation of coherent current structures andtheir disruption through magnetic reconnection. We present automatic techniquesaimed at detecting reconnection events in large data set of numericalsimulations. 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 ourtechniques use also a threshold on the aspect ratio of the regions selected. Wetest the performance of our algorithms. We propose an optimal aspect ratio tobe used in the automated machine learning algorithm: AR=18. The performance ofthe unsupervised approach results to be strongly competitive with respect tothose of other methods based on thresholds of standard reconnection proxies.

Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques