Author: Degtiarenko, P.
Paper Title Page
THPV043 Using AI for Management of Field Emission in SRF Linacs 970
  • A. Carpenter, P. Degtiarenko, R. Suleiman, C. Tennant, D.L. Turner, L.S. Vidyaratne
    JLab, Newport News, Virginia, USA
  • K.M. Iftekharuddin, M. Rahman
    ODU, Norfolk, Virginia, USA
  Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177.
Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year’s operational program and initial implementation of AI models will be deployed. Preliminary results and future plans are presented.
poster icon Poster THPV043 [1.857 MB]  
DOI • reference for this paper ※  
About • Received ※ 08 October 2021       Revised ※ 21 October 2021       Accepted ※ 21 November 2021       Issue date ※ 14 December 2021
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