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Machine Learning for Longitudinal Monitoring and Control of Ultra-Bright Electron Beams

Lundquist, Johan LU (2026)
Abstract
In this thesis machine learning applications to modern particle accelerators are developed and tested at a number of facilities. The focus is on improving and advancing longitudinal diagnostics, with a major project focusing on non-destructive predictions of beam distributions in longitudinal phase space. Applications are developed using data collected from MAX IV, FERMI, SwissFEL and FLASH. Beyond this, the use of machine learning for optimization of linear accelerators is implemented with focus on complex tasks such as longitudinal phase space and emittance optimization.

Further, we advance the use of longitudinal phase space diagnostics in connection with other studies focused on longitudinal beam dynamics. Projects include... (More)
In this thesis machine learning applications to modern particle accelerators are developed and tested at a number of facilities. The focus is on improving and advancing longitudinal diagnostics, with a major project focusing on non-destructive predictions of beam distributions in longitudinal phase space. Applications are developed using data collected from MAX IV, FERMI, SwissFEL and FLASH. Beyond this, the use of machine learning for optimization of linear accelerators is implemented with focus on complex tasks such as longitudinal phase space and emittance optimization.

Further, we advance the use of longitudinal phase space diagnostics in connection with other studies focused on longitudinal beam dynamics. Projects include investigations into the high-order shaping of the longitudinal phase space and the further development of beam compression schemes, including experimental measurements of arc-like bunch compressors and investigations of their inherent advantages. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Doctor Ischebeck, Rasmus, Paul Scherrer Institute, Villigen, Switzerland
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Accelerators, Beam diagnostics, Machine Learning, Beam dynamics, Artificial neural network, Free electron laser, supervised learning, transverse deflecting structure
pages
99 pages
publisher
Lund University
defense location
Rydbergsalen, Fysicum.
defense date
2026-10-16 13:15:00
ISBN
978-91-6858-031-9
978-91-6858-030-2
language
English
LU publication?
yes
id
69cf630a-c4f4-4541-9e85-a37dceef25ae
date added to LUP
2026-08-31 16:46:14
date last changed
2026-09-24 13:29:17
@phdthesis{69cf630a-c4f4-4541-9e85-a37dceef25ae,
  abstract     = {{In this thesis machine learning applications to modern particle accelerators are developed and tested at a number of facilities. The focus is on improving and advancing longitudinal diagnostics, with a major project focusing on non-destructive predictions of beam distributions in longitudinal phase space. Applications are developed using data collected from MAX IV, FERMI, SwissFEL and FLASH. Beyond this, the use of machine learning for optimization of linear accelerators is implemented with focus on complex tasks such as longitudinal phase space and emittance optimization. <br/><br/>Further,  we advance the use of longitudinal phase space diagnostics in connection with other studies focused on longitudinal beam dynamics. Projects include investigations into the high-order shaping of the longitudinal phase space and the further development of beam compression schemes, including experimental measurements of arc-like bunch compressors and investigations of their inherent advantages.}},
  author       = {{Lundquist, Johan}},
  isbn         = {{978-91-6858-031-9}},
  keywords     = {{Accelerators; Beam diagnostics; Machine Learning; Beam dynamics; Artificial neural network; Free electron laser; supervised learning; transverse deflecting structure}},
  language     = {{eng}},
  month        = {{09}},
  publisher    = {{Lund University}},
  school       = {{Lund University}},
  title        = {{Machine Learning for Longitudinal Monitoring and Control of Ultra-Bright Electron Beams}},
  url          = {{https://lup.lub.lu.se/search/files/261073963/Thesis_JohanL_FinalPrint_Kappa.pdf}},
  year         = {{2026}},
}