Preprints
https://doi.org/10.5194/angeo-2021-33
https://doi.org/10.5194/angeo-2021-33

  03 Jun 2021

03 Jun 2021

Review status: a revised version of this preprint was accepted for the journal ANGEO and is expected to appear here in due course.

Unsupervised classification of simulated magnetospheric regions

Maria Elena Innocenti1, Jorge Amaya2, Joachim Raeder3, Romain Dupuis2, Banafsheh Ferdousi3, and Giovanni Lapenta2 Maria Elena Innocenti et al.
  • 1Institut für Theoretische Physik, Ruhr-Universität Bochum, Bochum, Germany
  • 2Centre for mathematical Plasma Astrophysics, Department of Mathematics, KU Leuven, Leuven, Belgium
  • 3Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA

Abstract. In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or opera- tional interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

Maria Elena Innocenti et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on angeo-2021-33', Anonymous Referee #1, 23 Jun 2021
    • AC1: 'Reply on RC1', Maria Elena Innocenti, 08 Jul 2021
      • AC2: 'Reply on AC1 / 2', Maria Elena Innocenti, 14 Jul 2021
  • RC2: 'Comment on angeo-2021-33', Anonymous Referee #2, 01 Jul 2021
    • AC3: 'Reply on RC2', Maria Elena Innocenti, 14 Jul 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on angeo-2021-33', Anonymous Referee #1, 23 Jun 2021
    • AC1: 'Reply on RC1', Maria Elena Innocenti, 08 Jul 2021
      • AC2: 'Reply on AC1 / 2', Maria Elena Innocenti, 14 Jul 2021
  • RC2: 'Comment on angeo-2021-33', Anonymous Referee #2, 01 Jul 2021
    • AC3: 'Reply on RC2', Maria Elena Innocenti, 14 Jul 2021

Maria Elena Innocenti et al.

Maria Elena Innocenti et al.

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Short summary
Spacecraft missions do not always record observations at the highest possible resolution: the so-called burst mode is switched on only occasionally. It is of paramount importance that processes of interest are sampled in burst mode. At the moment, many missions rely on a "scientist in the loop", that decides when to trigger burst mode by looking at preview data. Our work constitutes a first step towards making this decision automatic, to improve mission operations and decrease human bias.