Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks

Abstract

Magnetic reconnection is a fundamental process that quickly releases magnetic energy stored in a plasma.Identifying, from simulation outputs, where reconnection is taking place is non-trivial and, in general, has to be performed by human experts. Hence, it would be valuable if such an identification process could be automated. Here, we demonstrate that a machine learning algorithm can help to identify reconnection in 2D simulations of collisionless plasma turbulence. Using a Hybrid Vlasov Maxwell (HVM) model, a data set containing over 2000 potential reconnection events was generated and subsequently labeled by human experts. We test and compare two machine learning approaches with different configurations on this data set. The best results are obtained with a convolutional neural network (CNN) combined with an 'image cropping' step that zooms in on potential reconnection sites. With this method, more than 70% of reconnection events can be identified correctly. The importance of different physical variables is evaluated by studying how they affect the accuracy of predictions. Finally, we also discuss various possible causes for wrong predictions from the proposed model.

16 pages, 9 figures and 5 tabels

References in corpus (1)

• Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions

Cited by in corpus (3)

• Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques
• Importance of accurate consideration of the electron inertia in hybrid-kinetic simulations of collisionless plasma turbulence: 1. The 2D limit
• Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning