% 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”.

@ARTICLE{Korten:639300,
      author       = {Korten, Till and Rybnikov, Vladimir and Steinbach, Peter
                      and Mirian, Najmeh},
      title        = {{V}irtual pulse reconstruction diagnostic for single-shot
                      measurement of free electron laser radiation power},
      journal      = {Physical review accelerators and beams},
      volume       = {28},
      number       = {3},
      issn         = {2469-9888},
      address      = {College Park, MD},
      publisher    = {American Physical Society},
      reportid     = {PUBDB-2025-04401, arXiv:2411.17644},
      pages        = {030703},
      year         = {2025},
      note         = {9 pages , 5 figures},
      abstract     = {Accurate characterization of radiation pulse profiles is
                      crucial for optimizing beam quality and enhancing
                      experimental outcomes in free electron laser (FEL) research.
                      In this paper, we present a unique approach that employs
                      machine learning techniques for real-time virtual
                      diagnostics of FEL radiation pulses. Our simple artificial
                      intelligence (AI)-based diagnostic tool utilizes
                      longitudinal phase space data obtained from the X-band
                      transverse deflecting structure to reconstruct the temporal
                      profile of FEL pulses in real time. Unlike traditional
                      single-shot methods, this AI-driven solution provides a
                      noninvasive, highly efficient alternative for pulse
                      characterization. By leveraging state-of-the-art machine
                      learning models, our method facilitates precise, single-shot
                      measurements of FEL pulse power, offering significant
                      advantages for FEL science research. This work outlines the
                      conceptual framework, methodology, and validation results of
                      our virtual diagnostic tool, demonstrating its potential to
                      significantly impact FEL research.},
      cin          = {FTX},
      ddc          = {530},
      cid          = {I:(DE-H253)FTX-20210408},
      pnm          = {621 - Accelerator Research and Development (POF4-621)},
      pid          = {G:(DE-HGF)POF4-621},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2411.17644},
      howpublished = {arXiv:2411.17644},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2411.17644;\%\%$},
      doi          = {10.1103/PhysRevAccelBeams.28.030703},
      url          = {https://bib-pubdb1.desy.de/record/639300},
}