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| Dissertation / PhD Thesis | PUBDB-2024-01513 |
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2023
Hamburg
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Please use a persistent id in citations: urn:nbn:de:gbv:18-ediss-117247 doi:10.3204/PUBDB-2024-01513
Abstract: Laser-plasma accelerators (LPA) utilize intense laser pulses to drive plasma waves, generating strong electric fields capable of accelerating electrons to relativistic energies. This technology promises accelerator facilities that are orders of magnitude smaller than their conventional counterparts. However, the compactness of LPA systems and the non-linearity of the laser-plasma interaction create a highly coupled environment, where various input and output parameters intricately interconnect. Consequently, manually identifying suitable working points providing the desired beam quality and stability as well as precise tuning of beam parameters to match the demands of specific applications becomes a tedious task, often leading to sub-optimal performance.The aim of this thesis is to explore the application of machine learning-based methods, particularly Bayesian optimization, within the realm of laser-plasma accelerators. The study involves the implementation of Bayesian optimization to fine-tune the parameters of the Lux accelerator, encompassing simulations and real-time experimentation.The proposed approach is initially examined through particle-in-cell simulations of the accelerator. After optimizing the system for beam quality, the predictive model built by the optimizer is employed to identify sources of instability and explore potential avenues for improvement. Subsequently, Bayesian optimization is implemented for online control of the experiment. Various methods to address experimental noise and parameter fluctuations are investigated, leading to the identification of techniques emphasizing ultimate beam quality or stability. This way the machine can autonomously tune itself to generate electron beams with sub-percent energy spread and significant charge. Furthermore, a robust operational regime with less than five percent energy spread for 90 % of all shots is identified and studied.Finally, Bayesian optimization targeting multiple beam parameters enables the identification of tuning curves allowing precise adjustments to specific beam properties while maintaining optimal beam quality. These complex tuning curves describe the intricate balancing of multiple laser and plasma parameters to achieve the most favorable tuning of beam parameters. This approach facilitates charge tuning over nearly 100 pC for various beam energies, while keeping energy spreads below 5 %.In combination, the methods presented in this thesis provide valuable tools for effectively managing the inherent complexity of LPAs, spanning from the design phase in simulations to real-time operation, potentially paving the way for LPAs to cater to a wide array of applications with diverse demands.
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