Identification of Electron Diffusion Regions with a Machine Learning Approach on MMS Data at the Earth's Magnetopause

This article presents 18 magnetic reconnection electron diffusion region (EDR) candidates found using a neural network algorithm with the Magnetospheric Multiscale Mission phase 1a data at the Earth's dayside magnetopause. These new candidates are compared to the 32 previously reported dayside EDRs listed in Webster et al. (2018), , which constitute the training database of our algorithm. One of the main parameters used is a scalar quantity called MeanRL which is based on the asymmetry of the electron velocity distribution function and better identifies electron agyrotropy in the plane perpendicular to the magnetic field. In the light of the new EDR candidates found, we discuss and analyze the sign of the energy dissipation during the reconnection process and the distinction between the inner and outer EDRs, with 40% of the candidates showing negative or oscillating dissipation. We also present in details one of the new identified EDR candidates.

Automated summary

This article identifies 18 magnetic reconnection electron diffusion region (EDR) candidates at the Earth's dayside magnetopause using a neural network algorithm, comparing them with previously reported EDRs and discussing the energy dissipation during the reconnection process as well as the distinction between inner and outer EDRs.