Dresden 2026 – scientific programme
Parts | Days | Selection | Search | Updates | Downloads | Help
AKBP: Arbeitskreis Beschleunigerphysik
AKBP 13: Novel Accelerators II
AKBP 13.3: Talk
Thursday, March 12, 2026, 12:00–12:15, SCH/A117
Statistical analysis of sources of instability in electron beam quality in laser plasma accelerators preparing for Bayesian optimization — •Franziska Marie Herrmann1,2, Maxwell Laberge1, Yen-Yu Chang1, Amin Ghaith1, Jeffrey Kelling1, Susanne Schöbel1, Patrick Ufer1,2, Ulrich Schramm1,2, and Arie Irman1 — 1HZDR, Dresden, Germany — 2Technische Universität Dresden, Dresden, Germany
Laser-electron accelerators are emerging as novel, compact sources of high-quality relativistic electron beams for a wide range of applications. Each experimental application requires unique electron parameters. Additionally, all the input parameters are interconnected, resulting in a highly complex parameter space. To address this issue, we have developed a semi-automated Bayesian optimization loop that adjusts six input parameters simultaneously to achieve optimal electron beam parameters. However, the high nonlinearity of laser wakefield acceleration poses a challenge for automated optimization, as even minor fluctuations in input parameters can lead to significant changes in electron beam properties. To quantify and mitigate the effects of these statistical fluctuations, we have compiled an extensive dataset through systematic studies of their characteristics and influence on the electron beam quality. Alongside demonstrating an initial prototype for semi-automated Bayesian optimization, this work will enhance the understanding of the underlying sources of instability in laser-plasma acceleration experiments, which are essential for more complex machine learning experiments.
Keywords: Laser electron acceleration; Machine Learning; Stability