EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

EDR signatures observed by MMS : a statistical study of dayside events found with machine learning

Quentin Lenouvel1, Vincent Génot4, Philippe Garnier5, Benoit Lavraud3, and Sergio Toledo2
Quentin Lenouvel et al.
  • 1CNRS, Physics, France (
  • 2University of Murcia, Murcia, Spain
  • 3IRAP/CNRS, Toulouse, France
  • 4IRAP / CNRS / UPS, PEPS, Toulouse
  • 5Universite Paul Sabatier, Toulouse, France

The understanding of magnetic reconnection's physical processes has considerably been improved thanks to the data of the Magnetopsheric Multiscale mission (MMS). However, a lot of work still has to be done to better characterize the core of the reconnection process : the electron diffusion region (EDR). We previously developed a machine learning algorithm to automatically detect EDR candidates, in order to increase the available list of events identified in the literature. However, identifying the parameters that are the most relevant to describe EDRs is complex, all the more that some of the small scale plasma/fields parameters show limitations in some configurations such as for low particle densities or large guide fields cases. In this study, we perform a statistical study of previously reported dayside EDRs as well as newly reported EDR candidates found using machine learning methods. We also show different single and multi-spacecraft parameters that can be used to better identify dayside EDRs in time series from MMS data recorded at the magnetopause. And finally we show an analysis of the link between the guide field and the strength of the energy conversion around each EDR.

How to cite: Lenouvel, Q., Génot, V., Garnier, P., Lavraud, B., and Toledo, S.: EDR signatures observed by MMS : a statistical study of dayside events found with machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2381,, 2021.


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