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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TUPOST027 |
Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities |
915 |
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- J.A. Diaz Cruz, S. Biedron
UNM-ECE, Albuquerque, USA
- J.A. Diaz Cruz
SLAC, Menlo Park, California, USA
- R. Pirayesh
UNM-ME, Albuquerque, New Mexico, USA
- S. Sosa
ODU, Norfolk, Virginia, USA
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The multiple systems involved in the operation of particle accelerators use diverse control systems to reach the desired operating point for the machine. Each system needs to tune several control parameters to achieve the required performance. Traditional Low-Level RF (LLRF) systems are implemented as proportional-integral feedback loops, whose gains need to be optimized. In this paper, we explore Machine Learning (ML) as a tool to improve a traditional LLRF controller by tuning its gains using a Neural Network (NN). We present the data production scheme and a control parameter optimization using a NN. The NN training is performed using the THETA supercomputer.
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DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST027
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About • |
Received ※ 14 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 20 June 2022 |
Cite • |
reference for this paper using
※ BibTeX,
※ LaTeX,
※ Text/Word,
※ RIS,
※ EndNote (xml)
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