Investigation of Particle Dynamics in Complex Plasma with PK-4

Abstract

Complex plasmas are low temperature plasmas containing micrometer-sized charged particles that exhibit Coulomb interactions. When introduced into the plasma environment, these particles acquire a significant negative charge by collecting electrons and ions. This leads to the formation of coupled systems where collective effects such as crystallization, phase transitions, and wave propagation can be observed directly at the particle scale. The PK-4 experiment offers a unique platform for investigating these phenomena under well-controlled laboratory conditions. Experiments can be conducted both on the ground and in microgravity environments, such as during parabolic flight campaigns and aboard the International Space Station. This thesis presents a detailed investigation of particle dynamics in complex plasmas using the PK-4 facility. The primary focus is placed on the behavior of dust acoustic waves, electrorheological effects, and the ion drag force. To address the challenges of detecting fastmoving and weakly illuminated particles to ensure a statistically significant data analysis, advanced image processing techniques and automated evaluation methods were developed. These include the use of high speed digital cameras and machine learning–enhanced tools for signal enhancement and trajectory reconstruction. One of the key findings is the identification and reconstruction of tilted wavefronts in dust acoustic waves. These wavefronts exhibit deviations from ideal theoretical predictions due to the combined influence of the gravitational force and boundary effects. A spatial shift in the wave structure, leading to asymmetries in propagation, is shown to be induced by additional electric fields. Furthermore, the influence of electrorheological effects on wave propagation under microgravity conditions was confirmed through experimental observations and charge distribution modeling. For the first time, crystallization was observed in a particle system that remained in motion throughout the process. The formation of string-like particle arrangements was found to be linked to charge distributions shaped by electrorheological interactions. Finally, the ion drag force acting on individual microparticles was quantified using a data driven approach based on Bayesian optimization. This method allowed for the extraction of key plasma parameters and provided validation of analytical force models, yielding deeper insight into charge dynamics and particle ion interactions. In conclusion, the integration of modern data science techniques with experimental plasma physics conducted under well-controlled conditions has led to significant improvements in measurement precision. This, in turn, has enhanced the understanding of fundamental plasma physical processes. The methods and results presented in this work contribute to a deeper insight into the behavior of complex plasmas and lay the groundwork for future investigations in both terrestrial and space based plasma environments. The published open-source methods provide a flexible framework for the implementation of additional models and can be used to analyze future experiments, enabling the generation of higher statistical significance.