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Identification of Electron Diffusion Regions with a Machine Learning Approach on MMS Data at the Earth's Magnetopause
Univ Toulouse 3, CNRS, Inst Rech Astrophys & Planetol, Ctr Natl Etud Spatiales, Toulouse, France..
Univ Toulouse 3, CNRS, Inst Rech Astrophys & Planetol, Ctr Natl Etud Spatiales, Toulouse, France..
Univ Toulouse 3, CNRS, Inst Rech Astrophys & Planetol, Ctr Natl Etud Spatiales, Toulouse, France..
Univ Toulouse 3, CNRS, Inst Rech Astrophys & Planetol, Ctr Natl Etud Spatiales, Toulouse, France.;Univ Murcia, Dept Electromagnetism & Elect, Murcia, Spain..
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2021 (English)In: Earth and Space Science, E-ISSN 2333-5084, Vol. 8, no 5, article id e2020EA001530Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
American Geophysical Union (AGU) , 2021. Vol. 8, no 5, article id e2020EA001530
Keywords [en]
EDR, machine learning, magnetic reconnection, magnetopause, MMS, neural network
National Category
Fusion, Plasma and Space Physics
Identifiers
URN: urn:nbn:se:kth:diva-298095DOI: 10.1029/2020EA001530ISI: 000658287100004Scopus ID: 2-s2.0-85105282050OAI: oai:DiVA.org:kth-298095DiVA, id: diva2:1575181
Note

QC 20210629

Available from: 2021-06-29 Created: 2021-06-29 Last updated: 2022-06-25Bibliographically approved

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Lindqvist, Per-Arne

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