Abstract. 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.
Context. Solar Orbiter provides dust detection capability in the inner heliosphere, but estimating physical properties of detected dust from the collected data is far from straightforward. Aims. First, a physical model for dust collection considering a Poisson process is formulated. Second, it is shown that dust on hyperbolic orbits is responsible for the majority of dust detections with Solar Orbiter’s Radio and Plasma Waves (RPW). Third, the model for dust counts is fitted to Solar Orbiter RPW data and parameters of the dust are inferred, namely radial velocity, hyperbolic meteoroids predominance, and the solar radiation pressure to gravity ratio as well as the uncertainties of these. Methods. Nonparametric model fitting was used to get the difference between the inbound and outbound detection rate and dust radial velocity was thus estimated. A hierarchical Bayesian model was formulated and applied to available Solar Orbiter RPW data. The model uses the methodology of integrated nested Laplace approximation, estimating parameters of dust and their ncertainties. Results. Solar Orbiter RPW dust observations can be modeled as a Poisson process in a Bayesian framework and observations up to this date are consistent with the hyperbolic dust model with an additional background component. Analysis suggests a radial velocity of the hyperbolic component around (63 ± 7) km s−1 with the predominance of hyperbolic dust being about (78 ± 4)%. The results are consistent with hyperbolic meteoroids originating between 0.02 AU and 0.1 AU and showing substantial deceleration, which implies effective solar radiation pressure to a gravity ratio ≳ 0.5. The flux of the hyperbolic component at 1 AU is found to be (1.1 ± 0.2) × 10−4 m−2s−1 and the flux of the background component at 1 AU is found to be (5.4 ± 1.5) × 10−5 m−2s−1.
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.
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