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BiBTeX citation export for WEPV007: Machine Learning Projects at the 1.5-GeV Synchroton Light Source DELTA

  author       = {D. Schirmer and A. Althaus and S. Hüser and S. Khan and T. Schüngel},
  title        = {{Machine Learning Projects at the 1.5-GeV Synchroton Light Source DELTA}},
  booktitle    = {Proc. ICALEPCS'21},
  pages        = {631--635},
  eid          = {WEPV007},
  language     = {english},
  keywords     = {controls, storage-ring, injection, synchrotron, electron},
  venue        = {Shanghai, China},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {18},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {03},
  year         = {2022},
  issn         = {2226-0358},
  isbn         = {978-3-95450-221-9},
  doi          = {10.18429/JACoW-ICALEPCS2021-WEPV007},
  url          = {https://jacow.org/icalepcs2021/papers/wepv007.pdf},
  abstract     = {{In recent years, several machine learning (ML) based projects have been developed to support automated monitoring and operation of the DELTA electron storage ring facility. This includes self-regulating global and local orbit correction of the stored electron beam, betatron tune feedback as well as electron transfer rate (injection) optimization. Furthermore, the implementation for a ML-based chromaticity control is currently prepared. Some of these processes were initially simulated and then successfully transferred to real machine operation. This report provides an overview of the current status of these projects.}},