Author: Jacobson, B.T.
Paper Title Page
MOPOPT058 Machine Learning Training for HOM Reduction in a TESLA-Type Cryomodule at FAST 400
SUSPMF099   use link to see paper's listing under its alternate paper code  
 
  • J.A. Diaz Cruz
    UNM-ECE, Albuquerque, USA
  • J.A. Diaz Cruz, A.L. Edelen, B.T. Jacobson, J.P. Sikora
    SLAC, Menlo Park, California, USA
  • D.R. Edstrom, A.H. Lumpkin, R.M. Thurman-Keup
    Fermilab, Batavia, Illinois, USA
 
  Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and the Beam Position Monitor (BPM) measurements downstream the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals. Here, we present a couple of Neural Networks (NN) for bunch-by-bunch mean prediction and standard deviation prediction for BPMs located downstream the CM.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058  
About • Received ※ 15 June 2022 — Revised ※ 18 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022
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