% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Jalas:605571,
      author       = {Jalas, Soeren},
      othercontributors = {Maier, Andreas and Leemans, Wim},
      title        = {{M}achine {L}earning {B}ased {O}ptimization of
                      {L}aser-{P}lasma {A}ccelerators},
      school       = {Universität Hamburg},
      type         = {Dissertation},
      address      = {Hamburg},
      reportid     = {PUBDB-2024-01513},
      pages        = {156},
      year         = {2023},
      note         = {Publish under creative commons Attribution-NonCommercial
                      4.0 license
                      (https://creativecommons.org/licenses/by-nc/4.0/);
                      Dissertation, Universität Hamburg, 2024},
      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.},
      cin          = {MLS},
      cid          = {I:(DE-H253)MLS-20210107},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      PHGS, VH-GS-500 - PIER Helmholtz Graduate School
                      $(2015_IFV-VH-GS-500)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2015_IFV-VH-GS-500$},
      experiment   = {EXP:(DE-H253)LUX-Beamline-20221201},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:gbv:18-ediss-117247},
      doi          = {10.3204/PUBDB-2024-01513},
      url          = {https://bib-pubdb1.desy.de/record/605571},
}