Dust observations with antenna measurements and its prospects for observations with Parker Solar Probe and Solar Orbiter

The electric and magnetic field instrument suite FIELDS on board the NASA Parker Solar Probe and the radio and plasma waves instrument RPW on the ESA Solar Orbiter mission that explore the inner heliosphere are sensitive to signals generated by dust impacts. Dust impacts have been observed using electric field antennas on spacecraft since the 1980s and the method was recently used with a number of space missions to derive dust fluxes. Here, we consider the details of dust impacts, subsequent development of the impact generated plasma and how it produces the measured signals. We describe empirical approaches to characterise the signals and compare these in a qualitative discussion of laboratory simulations to predict signal shapes for spacecraft measurements in the inner solar system. While the amount of charge production from a dust impact will be higher near the Sun than observed in the interplanetary medium before, the amplitude of pulses is determined by the recovery behaviour that is different near the Sun since it varies with the plasma environment

A comparison of contact charging and impact ionization in low-velocity impacts: implications for dust detection in space

We investigate the generation of charge due to collision between projectiles with sizes below ∼1 µm and metal surfaces at speeds ∼0.1 to 10 km s−1. This corresponds to speeds above the elastic limit and well below speeds where volume ionization can occur. Impact charge production at these low to intermediate speeds has traditionally been described by invoking the theory of shock wave ionization. By looking at the thermodynamics of the low-velocity solution of shock wave ionization, we find that such a mechanism alone is not sufficient to account for the recorded charge production in a number of scenarios in the laboratory and in space. We propose a model of capacitive contact charging that involves no direct ionization, in which we allow for projectile fragmentation upon impact. Furthermore, we show that this model describes measurements of metal–metal impacts in the laboratory well. We also address contact charging in the context of ice-on-metal collisions and apply our results to rocket observations of mesospheric dust. In general, we find that contact charging dominates at speeds of up to a few kilometres per second and complements shock wave ionization up to speeds where direct ionization can take place. The conditions that we consider can be applied to dust particles naturally occurring in space and in Earth's upper atmosphere and their direct impacts on rockets, spacecraft, and impacts of secondary ejecta

One-Year Analysis of Dust Impact-Like Events Onto the MMS Spacecraft

We present an analysis of 1‐year data of dust impacts observed on two of the Earth‐orbiting Magnetospheric Multiscale mission (MMS) spacecraft. The dust impact signals were identified in observations of the electric field probes and were registered simultaneously by monopole and dipole configurations of the instrument. This unique setup allows us to reliably identify changes in the spacecraft potential as candidates for dust impacts. We present a detailed study of the properties of the pulses generated by the dust impacts and show the influence of the local plasma environment (spacecraft location in the Earth magnetosphere) on signals generated by dust impacts and their detection. We discuss the credibility of impact identification and possible sources of signal misinterpretation. We find a total of 784 observed events that we can interpret as dust impacts and that we use to derive a dust flux. We show that MMS1 registered 0.7 and MMS3 0.8 dust impact‐like events per hour. This corresponds to dust flux of 2.5–6 × 10−5 m−2 s−1

Ion Cloud Expansion after Hypervelocity Dust Impacts Detected by the MMS Electric Probes in the Dipole Configuration

Dust impact detection by electric field instruments is a well-established technique. On the other hand, not all aspects of signal generation by dust impacts are completely understood. We present a study of events related to dust impacts on the spacecraft body detected by electric field probes operating simultaneously in the monopole (probe-to-spacecraft potential measurement) and dipole (probe-to-probe potential measurement) configurations by the Earth-orbiting Magnetospheric Multiscale mission spacecraft. This unique measurement allows us to investigate connections between monopole and dipole data. Our analysis shows that the signal detected by the electric field instrument in a dipole configuration is generated by an ion cloud expanding along the electric probes. In this case, expanding ions affect not only the potential of the spacecraft body but also one or more electric probes at the end of antenna booms. Electric probes located far from the spacecraft body can be influenced by an ion cloud only when the spacecraft is located in tenuous ambient plasma inside of the Earth's magnetosphere. Derived velocities of the expanding ions on the order of tens of kilometers per second are in the range of values measured experimentally in the laboratory

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

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