0567533 - ÚFA 2024 RIV DE eng J - Journal Article
Kvammen, A. - Wickstrøm, K. - Kociscak, S. - Vaverka, J. - Nouzák, L. - Zaslavsky, A. - Rackovic-Babic, K. - Gjelsvik, A. - Píša, David - Souček, Jan - Mann, I.
Machine learning detection of dust impact signals observed by the Solar Orbiter.
Annales Geophysicae. Roč. 41, č. 1 (2023), s. 69-86. ISSN 0992-7689. E-ISSN 1432-0576
R&D Projects: GA ČR(CZ) GA22-10775S
Institutional support: RVO:68378289
Keywords : waves * interplanetary
OECD category: Astronomy (including astrophysics,space science)
Impact factor: 1.9, year: 2022
Method of publishing: Open access
https://angeo.copernicus.org/articles/41/69/2023/

This article presents the results of 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 impacts the spacecraft at high velocity. In this way, ∼ 5–20 dust impacts are daily detected as the Solar Orbiter travels through the interplanetary 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 2 years of Radio and Plasma Waves instrument data.
Overall, we conclude that detection of dust impact signals is a suitable task for supervised machine learning techniques. The convolutional neural network achieves the highest performance with 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 classifiers detect more dust particles (on average) than the on-board classification algorithm, with 16 % ± 1 % and 18 % ± 8 % 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.
Permanent Link: https://hdl.handle.net/11104/0338784