In this paper a search routine in MATLAB was developed in order to find and analyze oblique whistler waves in the data from the ESA and NASA spacecraft Solar Orbiter. Oblique whistler waves are a type of plasma wave which propagate at an angle with respect to the background magnetic field. They are efficient at scattering electrons in the solar wind but their role in interplanetary coronal mass ejections (ICMEs) is yet unknown. Magnetic field data from 1-31st of June 2022, as well as the 24th and 27-28th of January 2022 was examined. The search routine found six whistler waves in June and 12 for the dates in January. Among these, all found whistler waves were found in the sheath region of the ICMEs, and all had a plasma beta > 1. However due to instrumental artefacts the values of θk were found to be smaller than detected by the search routine, indicating less obliqueness than first expected. Some of the whistler waves seemed to have an obliqueness that changed with time and the bandwidth of the waves varied among the identified.

This project aims to investigate neural network systems as an effective tool for the in-space captured dust impact signal detection. Cosmic dust is the nanometre to micrometre fine-sized particles that exist in the interplanetary region. They originate from comets, asteroids, the planets and their moons and rings, or even the interstellar region. Some are visible to the human eye as, for instance, zodiacal light. However, most dust grains are sparsely spread in space and can be captured only by in-situ measurements. One method to capture such a small grain in space utilizes the whole spacecraft’s surface as a detector and uses the onboard electric field measurement to identify their impact signals. Those signals are highly non-linear and often identified manually. A neural network system is a possible solution to improve dust detection for a massive dataset.The European Space Agency’s (ESA) solar physics mission, Solar Orbiter, has electric field measurement (PWI) onboard and can detect the micrometeorite impact signals near the sun. We used two types of PWI datasets to investigate the use of neural network systems in interplanetary dust detection.We first used a pre-trained neural network to the High-Frequency (HF) Time Domain Sampler (TDS) data to adapt further to the new dataset. We were able to obtain good detection classifications as the previous work except for the data with high time resolution, which has not been used for the pre-training before. Therefore, we implemented extra preprocessing to enable classification of data with high time resolution.We trained and tested another neural network on another type of PWI dataset, that is, the Low-Frequency (LF) continuous data. This data type is different from the TDS data type in that it does not come in packets but as a continuous data stream covering an entire day and has a lower sampling frequency. Which required different preprocessing-procedures.Based on the two types of neural network analysis we use above; we have finally been able to investigate the characteristics of dust distribution in the interplanetary region. Using the statistical analysis obtained by the SolO/PWI between April of 2020 to Mars of 2023, among others, the following characteristics have been found: The neural network analysed dust impact rates show a similar trend as onboard processed dust impact rates. Dust impact amplitude was found to be correlated to distance from the sun, spacecraft velocity, and spacecraft radial velocity. The impact rate increases as the spacecraft travels sunward. Much of the dust appears to have speeds lower than the spacecraft. Overall, from this study, we concluded that the HF neural network is better in dust signal detection, but the LF network can be improved. Shortcomings and possible improvements are presented in the conclusions.

Spacecraft experience electric currents on conductive materials exposed to sunlight, which introduces noise in scientific data. These currents are mainly due to the photoelectric effect and should therefore be proportional to the inverse square heliocentric distance. However, measurements on the Solar Orbiter spacecraft suggests that these currents deviate from this proportionality, especially at perihelia. This paper aims to improve the understanding of how and why these induced currents vary by creating a model to describe the phenomenon. The investigation was based on thermal bending, thermionic emission, the photoelectric effect, outgassing, and a temperature dependence of the work function. Through numerical approximation, the thermal bending of the approximately 6m modeled antennas was estimated to be almost three meters at perihelion and the estimated outgassing fit the secular change in the data well. The direct impact of thermionic emissions was determined to be negligible. The final model was created utilizing a secular fit of the outgassing, the variation in the cross-section due to thermal bending, a yield proxy was created to model the impact of the work function temperature dependence, and the MgII index as a proxy for the solar EUV intensity. The final model was approximately accurate within 10%. Several future improvements are discussed, such as the inclusion of secondary emission or the empirical determination of the model deviation.