Machine learning detection of dust impact signals observed by the Solar Orbiter

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F23%3A00567533" target="_blank" >RIV/68378289:_____/23:00567533 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/23:10475653

Result on the web

<a href="https://angeo.copernicus.org/articles/41/69/2023/" target="_blank" >https://angeo.copernicus.org/articles/41/69/2023/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/angeo-41-69-2023" target="_blank" >10.5194/angeo-41-69-2023</a>

Result language

angličtina

Original language name

Machine learning detection of dust impact signals observed by the Solar Orbiter

Original language description

This article presents the results of automatic detection of dust impact signals observed by the Solar Orbiter – Radio and Plasma Waves instrument.nA 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.nIt 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.nIn 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.nOverall, 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.nThe 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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10308 - Astronomy (including astrophysics,space science)

Project

<a href="/en/project/GA22-10775S" target="_blank" >GA22-10775S: Analysis of plasma waves and dust grain impacts observed by RPW-TDS instrument on the Solar Orbiter spacecraft</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annales Geophysicae

ISSN

0992-7689

e-ISSN

1432-0576

Volume of the periodical

41

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

18

Pages from-to

69-86

UT code for WoS article

000922022100001

EID of the result in the Scopus database

2-s2.0-85147941328