Identifying magnetic reconnection in 2D-HVM simulations with CNNs

Abstract:

Magnetic reconnection is a fundamental process that quickly releases magnetic energy stored in a plasma. The occurrence of magnetic reconnection in turbulent plasmas and its interplay with a fully-developed turbulent state is still a matter of great debate. In general, the occurrence of magnetic reconnection 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 labelled by human experts using a workflow on zooniverse.org. This is a platform aimed at involving the general public in the labeling of scientific data sets. The project can be accessed via https://www.zooniverse.org/projects/taiyexingshang/magnetic-reconnection, together with a tutorial on how to identify reconnection sites. The project is public, so any expert can help with the labelling. Further information can be found on http://aida-space.eu/reconnection

Several machine learning approaches have been tested 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. Various possible causes for wrong predictions from the proposed model are also discussed.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA, www.aida-space.eu).