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@ARTICLE{Veglia:610908,
      author       = {Veglia, Bianca and Agapov, Ilya and Keil, Joachim},
      title        = {{N}eural {N}etworks for {ID} {G}ap {O}rbit
                      {D}istortion{C}ompensation in {PETRA} {III}},
      journal      = {Nuclear instruments $\&$ methods in physics research /
                      Section A},
      volume       = {1069},
      issn         = {0168-9002},
      address      = {Amsterdam},
      publisher    = {North-Holland Publ. Co.},
      reportid     = {PUBDB-2024-04731},
      pages        = {169934},
      year         = {2024},
      abstract     = {Undulators are used in storage rings to produce extremely
                      brilliant synchrotron radiation. In the ideal case, a
                      perfectly tuned undulator always has a first and second
                      field integrals equal to zero. But, in practice, field
                      integral changes during gap movements can never be avoided
                      for real-life devices. As they significantly impact the
                      circulating electron beam, there is the need to routinely
                      compensate such effects. Deep Neural Networks can be used to
                      predict the distortion in the closed orbit induced by the
                      undulator gap variations on the circulating electron beam.
                      In this contribution several current state-of-the-art deep
                      learning algorithms were trained on measurements from PETRA
                      III. The different architecture performances are then
                      compared to identify the best model for the gap-induced
                      distortion compensation.},
      cin          = {MPY},
      ddc          = {530},
      cid          = {I:(DE-H253)MPY-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) / 6G3
                      - PETRA III (DESY) (POF4-6G3) / ACCLAIM - Accelerating
                      Science with Artificial Intelligence and Machine Learning
                      (innovation pool) (ACCLAIM)},
      pid          = {G:(DE-HGF)POF4-621 / G:(DE-HGF)POF4-6G3 /
                      G:(DE-Ds200)ACCLAIM},
      experiment   = {EXP:(DE-H253)PETRAIII(machine)-20150101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001332686200001},
      doi          = {10.1016/j.nima.2024.169934},
      url          = {https://bib-pubdb1.desy.de/record/610908},
}