David Píša's research while affiliated with The Czech Academy of Sciences and other places

Solar Orbiter is equipped with electrical antennas performing fast measurements of the surrounding electric field. The antennas register high-velocity dust impacts through the electrical signatures of impact ionization. Although the basic principle of the detection has been known for decades, the understanding of the underlying process is not complete, due to the unique mechanical and electrical design of each spacecraft and the variability of the process. We present a study of electrical signatures of dust impacts on Solar Orbiter's body, as measured with the Radio and Plasma Waves electrical suite. A large proportion of the signatures present double-peak electrical waveforms in addition to the fast pre-spike due to electron motion, which are systematically observed for the first time. We believe this is due to Solar Orbiter's unique antenna design and a high temporal resolution of the measurements. The double peaks are explained as being due to two distinct processes. Qualitative and quantitative features of both peaks are described. The process for producing the primary peak has been studied extensively before, and the process for producing the secondary peak has been proposed before (Pantellini et al., 2012a) for Solar Terrestrial Relations Observatory (STEREO), although the corresponding delay of 100–300 µs between the primary and the secondary peak has not been observed until now. Based on this study, we conclude that the primary peak's amplitude is the better measure of the impact-produced charge, for which we find a typical value of around 8 pC. Therefore, the primary peak should be used to derive the impact-generated charge rather than the maximum. The observed asymmetry between the primary peaks measured with individual antennas is quantitatively explained as electrostatic induction. A relationship between the amplitude of the primary and the secondary peak is found to be non-linear, and the relation is partially explained with a model for electrical interaction through the antennas' photoelectron sheath.

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.

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.

The spectral fine structures of Saturn kilometric radiation (SKR) are best investigated with the wideband receiver (WBR) of Cassini's Radio and Plasma Wave Science (RPWS) instrument, with which measured radio fluxes can be displayed in time–frequency spectra with resolutions of 125 ms and ∼0.1 kHz. We introduce seven different classes of SKR fine structures ranging from dots (one class for 0-dimensional objects) over lines (four classes of 1-dimensional objects being horizontal, vertical, or with negative or positive slope) to areal features (one class for 2-dimensional objects). Additionally, we define a seventh class containing special structures named according to their appearance in time–frequency spectra. These special features are named rain, striations, worms, and caterpillar and the latter two have never been described in the literature so far. Using this newly defined classification scheme, we classify features in spectra at low frequencies in the baseband of the 80 kHz WBR and at medium frequencies around 325 kHz. A statistic of the occurrence of various classes and sub-classes shows some notable characteristics: lines with a positive slope are much more common at medium frequencies than at low frequencies and vertical lines are almost absent at low frequencies. The particular fine structure of striations (group of narrowbanded lines with predominantly negative slopes) is quite common below 80 kHz but less common near 325 kHz. At these medium frequencies, the lines rather look like interrupted striations which we term with the name “rain”. We also find rare instances of striations with a positive slope and rare instances of absorption signatures within areal features. The newly introduced sub-classes of worms (lines oscillating in frequency) and caterpillars occur almost exclusively below 80 kHz. Caterpillars have a typical bandwidth of ∼10 kHz, a constant frequency below ∼40 kHz for several hours and they are mostly observed beyond distances of 10 Saturn radii. We discuss the implications of our findings in view of the many theories about spectral fine structures of auroral radio emissions.

The spectral fine structures of Saturn kilometric radiation (SKR) are best investigated with the Wideband Receiver (WBR) of Cassini's Radio and Plasma Wave Science (RPWS) instrument, with which measured radio fluxes can be displayed in time-frequency spectra with resolutions of 125 ms and 0.1 kHz. We introduce seven different classes of SKR fine structures ranging from dots (one class for 0-dimensional objects) over lines (four classes of 1-dimensional objects being horizontal, vertical, or with negative or positive slope) to areal features (one class for 2-dimensional objects). Additionally, we define a 7th class containing special structures named according to their appearance in time-frequency spectra. These special features are named rain, striations, worms, and caterpillar, and the latter two have never been described in the literature so far. Using this newly defined classification scheme we classify features in spectra at low frequencies in the baseband of the 80-kHz WBR and at medium frequencies around 325 kHz. A statistics of the occurrence of various classes and sub-classes shows some notable characteristics: Lines with a positive slope are much more common at medium frequencies than at low frequencies, and vertical lines are almost absent at low frequencies. The particular fine structure of striations (group of narrowbanded lines with predominantly negative slopes) is quite common below 80 kHz, but less common near 325 kHz. At these medium frequencies, the lines rather look like interrupted striations, which we term with the name 'rain'. We also find rare instances of striations with a positive slope, and rare instances of absorption signatures within areal features. The newly introduced sub-classes of worms (lines oscillating in frequency) and caterpillar occur almost exclusively below 80 kHz. Caterpillars have a typical bandwidth of 10 kHz, a constant frequency below 40 kHz for several hours, and they are mostly observed beyond distances of 15 Saturn radii around local dusk. We discuss the implications of our findings in view of the many theories about spectral fine structures of auroral radio emissions.

Non-thermal radio emissions from Saturn, known as Saturn Kilometric Radiation (SKR), are analyzed for the Faraday rotation effect detected in Cassini RPWS High Frequency Receiver (HFR) observations. This phenomenon, which mainly affects the lower-frequency part of SKR below 200 kHz, is characterized by a rotation of the semi-major axis of the SKR polarization ellipse as a function of frequency during wave propagation through a birefringent plasma medium. Faraday rotation is found in 4.1% of all HFR data recorded by Cassini above 20 degrees northern and southern magnetic latitude, from mid-2004 to late 2017. A statistical visibility analysis shows that elliptically polarized SKR from the dawn source regions, when beamed towards high latitudes into the noon and afternoon local time sectors, is most likely to experience Faraday rotation along the ray path. The necessary conditions for Faraday rotation are discussed in terms of birefringent media and sharp plasma density gradients, where SKR (mostly R-X mode) gets split into the two circularly polarized modes R-X and L-O. By means of a case study we also demonstrate how Faraday rotation provides an estimate for the average plasma density along the ray path.

Auroral hiss emissions are ubiquitous in planetary magnetospheres, particularly in regions where electric current systems are present. They are generally diagnostic of electrodynamic coupling between conductive bodies, thus making auroral and moon‐connected magnetic field lines prime locations for their detection. However, the role of Saturn's rings as a dynamic conductive body has been elusive and of great interest to the community. Cassini's Grand Finale orbits afforded a unique opportunity to directly sample magnetic field lines connected to the main rings. Here we provide strong evidence for the persistent and organized presence of auroral hiss demonstrably associated with the main rings. This is in contrast to recent observations suggesting that Saturn's rings may be barriers to field‐aligned currents. Our results provide a new view of Saturn's rings as a dynamic system that is in continuous and ordered electrodynamic coupling with the planet.

The Cassini Grand Finale orbits offered a new view of Saturn and its environment owing to multiple highly inclined orbits with unprecedented proximity to the planet during closest approach. The Radio and Plasma Wave Science instrument detected striking signatures of plasma waves in the southern hemisphere. These all propagate in the whistler mode and are classified as (1) a filled funnel-shaped emission, commonly known as auroral hiss. Here however, our analysis indicates that they are likely associated with currents connected to the rings. (2) First observations of very low frequency saucers directly linked to the planet on field lines also connected to the rings. The latter observations are unique to low altitude orbits, and their presence at the Earth and Saturn alike shows that they are fundamental plasma waves in planetary ionospheres. Our results give an insight, from a unique perspective, into the dynamic and diverse nature of Saturn's environment.