EGU21-2659
https://doi.org/10.5194/egusphere-egu21-2659
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using clustering techniques for magnetic reconnection detection

Manuela Sisti1,2, Francesco Finelli2, Giorgio Pedrazzi3, Matteo Faganello1, Francesco Califano2, Francesca Delli Ponti3, and Valeriia Zalizniak3
Manuela Sisti et al.
  • 1Aix-Marseille University, CNRS, PIIM UMR 7345, France, EU (manuela.sisti@univ-amu.fr)
  • 2Dipartimento di Fisica, Università di Pisa, Italy, EU
  • 3HPC Department, Cineca, Bologna, Italy, EU

The formation of coherent current structures in turbulent collisionless magnetized plasmas and their disruption through magnetic reconnection has been extensively studied in past years via in situ observations, numerical simulations, and theoretical models. Presently there is no automatic verified way to detect reconnection events so that only an accurate human analysis can be performed. We set-up a machine learning unsupervised technique aimed at automatically detecting the presence of current sheet (CS) magnetic structures where reconnection is occurring. We make use of clustering techniques as KMeans and DBscan, and compare their efficiency to that of simpler methods which do not use machine learning but are only based on thresholds on important physical quantities. The unsupervised machine learning method turns out to be the one with the best performance. We applied these techniques to 2D kinetic HVM (Hybrid Vlasov Maxwell) plasma turbulence simulations, where ions evolve by solving the Vlasov equation while the electrons are treated as a fluid. Electron inertia is included. We are presently working on adapting our techniques to 1D time series extracted from our simulations aiming at reproducing typical data measured by satellites. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA, www.aida-space.eu).

How to cite: Sisti, M., Finelli, F., Pedrazzi, G., Faganello, M., Califano, F., Delli Ponti, F., and Zalizniak, V.: Using clustering techniques for magnetic reconnection detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2659, https://doi.org/10.5194/egusphere-egu21-2659, 2021.

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