Preprints
https://doi.org/10.5194/egusphere-2022-725
https://doi.org/10.5194/egusphere-2022-725
 
11 Aug 2022
11 Aug 2022

Machine Learning Detection of Dust Impact Signals Observed by The Solar Orbiter

Andreas Kvammen1, Kristoffer Wickstrøm1, Samuel Kociscak1, Jakub Vaverka2, Libor Nouzak2, Arnaud Zaslavsky3, Kristina Rackovic3,4, Amalie Gjelsvik1, David Pisa5, Jan Soucek5, and Ingrid Mann1 Andreas Kvammen et al.
  • 1Department of Physics and Technology, UiT The Arctic University of Norway, 9037, Tromsø, Norway
  • 2Department of Surface and Plasma Science, Charles University Prague, 18000, Prague, Czech Republic
  • 3LESIA – Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 5 place Jules Janssen, 92195, Meudon, France
  • 4Department of Astronomy, Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11000, Belgrade, Serbia
  • 5Institute of Atmospheric Physics, Czech Academy of Sciences, Bocni II/1401, 141 00 Prague, Czech Republic

Abstract. This article present results from automatic detection of dust impact signals observed by the Solar Orbiter – Radio and Plasma Waves instrument.

A sharp and characteristic electric field signal is observed by the Radio and Plasma Waves instrument when a dust particle impact the spacecraft at high velocity. In this way, ∼5–20 dust impacts are daily detected as the Solar Orbiter travels through the interstellar medium. The dust distribution in the inner solar system is largely uncharted and statistical studies of the detected dust impacts will enhance our understanding of the role of dust in the solar system.

It is however challenging to automatically detect and separate dust signals from the plural of other signal shapes for two main reasons. Firstly, since the spacecraft charging causes variable shapes of the impact signals and secondly because electromagnetic waves (such as solitary waves) may induce resembling electric field signals.

In this article, we propose a novel machine learning-based framework for detection of dust impacts. We consider two different supervised machine learning approaches: the support vector machine classifier and the convolutional neural network classifier. Furthermore, we compare the performance of the machine learning classifiers to the currently used on-board classification algorithm and analyze one and a half year of Radio and Plasma Waves instrument data.

Overall, we conclude that classification of dust impact signals is a suitable task for supervised machine learning techniques. In particular, the convolutional neural network achieves a 96 % ± 1 % overall classification accuracy and 94 % ± 2 % dust detection precision, a significant improvement to the currently used on-board classifier with 85 % overall classification accuracy and 75 % dust detection precision. In addition, both the support vector machine and the convolutional neural network detects more dust particles (on average) than the on-board classification algorithm, with 14 % ± 1 % and 16 % ± 7 % detection enhancement respectively.

The proposed convolutional neural network classifier (or similar tools) should therefore be considered for post-processing of the electric field signals observed by the Solar Orbiter.

Journal article(s) based on this preprint

24 Jan 2023
Machine learning detection of dust impact signals observed by the Solar Orbiter
Andreas Kvammen, Kristoffer Wickstrøm, Samuel Kociscak, Jakub Vaverka, Libor Nouzak, Arnaud Zaslavsky, Kristina Rackovic Babic, Amalie Gjelsvik, David Pisa, Jan Soucek, and Ingrid Mann
Ann. Geophys., 41, 69–86, https://doi.org/10.5194/angeo-41-69-2023,https://doi.org/10.5194/angeo-41-69-2023, 2023
Short summary

Andreas Kvammen et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-725', Anonymous Referee #1, 26 Sep 2022
    • AC1: 'Reply on RC1', Andreas Kvammen, 04 Nov 2022
  • RC2: 'Comment on egusphere-2022-725', Anonymous Referee #2, 03 Oct 2022
    • AC2: 'Reply on RC2', Andreas Kvammen, 04 Nov 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-725', Anonymous Referee #1, 26 Sep 2022
    • AC1: 'Reply on RC1', Andreas Kvammen, 04 Nov 2022
  • RC2: 'Comment on egusphere-2022-725', Anonymous Referee #2, 03 Oct 2022
    • AC2: 'Reply on RC2', Andreas Kvammen, 04 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (14 Nov 2022) by Gunter Stober
AR by Andreas Kvammen on behalf of the Authors (22 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to revisions (further review by editor and referees) (02 Dec 2022) by Gunter Stober
ED: Publish as is (05 Dec 2022) by Gunter Stober

Journal article(s) based on this preprint

24 Jan 2023
Machine learning detection of dust impact signals observed by the Solar Orbiter
Andreas Kvammen, Kristoffer Wickstrøm, Samuel Kociscak, Jakub Vaverka, Libor Nouzak, Arnaud Zaslavsky, Kristina Rackovic Babic, Amalie Gjelsvik, David Pisa, Jan Soucek, and Ingrid Mann
Ann. Geophys., 41, 69–86, https://doi.org/10.5194/angeo-41-69-2023,https://doi.org/10.5194/angeo-41-69-2023, 2023
Short summary

Andreas Kvammen et al.

Data sets

Data for Machine Learning Detection of Dust Impact Signals Observed by The Solar Orbiter Andreas Kvammen https://github.com/AndreasKvammen/ML_dust_detection

Model code and software

Code for Machine Learning Detection of Dust Impact Signals Observed by The Solar Orbiter Andreas Kvammen https://github.com/AndreasKvammen/ML_dust_detection

Andreas Kvammen et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Collisional fragmentation of asteroids, comets and meteoroids result in the formation of dust in the solar system. The dust distribution is however uncharted and the role of dust in the solar system is largely unknown. At present, the solar system is explored by the Solar Orbiter spacecraft. We present a novel method, based on artificial intelligence, that can be used for detecting when dust particles impact the Solar Orbiter with high accuracy, making it possible to study the dust distribution.