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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 Kvammen, Kristoffer Wickstrøm, Samuel Kociscak, Jakub Vaverka, Libor Nouzak, Arnaud Zaslavsky, Kristina Rackovic, Amalie Gjelsvik, David Pisa, Jan Soucek, and Ingrid Mann

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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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
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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.

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Collisional fragmentation of asteroids, comets and meteoroids result in the formation of dust in...
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