Paper |
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MOPOPT058 |
Machine Learning Training for HOM Reduction in a TESLA-Type Cryomodule at FAST |
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SUSPMF099 |
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- 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
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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.
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DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058
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About • |
Received ※ 15 June 2022 — Revised ※ 18 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022 |
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