Berlin 2024 – scientific programme
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AKBP: Arbeitskreis Beschleunigerphysik
AKBP 8: Beam Dynamics II
AKBP 8.6: Talk
Wednesday, March 20, 2024, 12:45–13:00, E 020
Towards experimental-tailored laser wakefield acceleration aided by Bayesian methods — •Franziska Marie Herrmann1,2, Tobias Hänel1,2, Susanne Schöbel1,2, Amin Ghaith1, Maxwell Laberge1, Yen-Yu Chang1, Patrick Ufer1,2, Paula Twellenkamp1,2, Teresa D'Orsi Barreto1,2, Jeffrey Kelling1, Arie Irman1, and Ulrich Schramm1,2 — 1Institut für Strahlenphysik, Helmholtz-Zentrum Dresden-Rossendorf, Deutschland — 2Technische Universität Dresden, Deutschland
In recent years, the demand for relativistic electrons is increasing emerging from various applications in material science, particle physics and medicine. This fuels the development of compact electron accelerators. One promising approach is laser wakefield acceleration, which harnesses the capability of plasma to sustain very high electric fields. As laser wakefield acceleration is driven by high-intensity lasers, it relies on a complex, non-linear interplay between drive lasers and plasma parameters, resulting in a broad parameter space for an optimum acceleration for the generation of high quality electron beams. For tailoring the electron beam to match the requirements of each application case, independent control of all the interconnected parameters is imperative. Because optimization relying solely on experimental data is expensive in both time and resources, we utilize Bayesian methods to optimise the acceleration process parameters and determine the Pareto front for each experiment, ensuring reproducible conditions on each operation day. This project will be a crucial step toward a stable laser-plasma accelerator with experiment-adapted electron beam properties.
Keywords: Electron Acceleration; Laser Wakefield Acceleration; Machine Learning; High Power Laser