Author: Lumpkin, A.H.
Paper Title Page
TUPOPT038 FAST-GREENS: A High Efficiency Free Electron Laser Driven by Superconducting RF Accelerator 1094
 
  • P. Musumeci, P.E. Denham, A.C. Fisher, Y. Park
    UCLA, Los Angeles, USA
  • R.B. Agustsson, T.J. Hodgetts, A.Y. Murokh, M. Ruelas
    RadiaBeam, Santa Monica, California, USA
  • L. Amoudry
    Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
  • D.R. Broemmelsiek, S. Nagaitsev, J. Ruan, J.K. Santucci, G. Stancari, A. Valishev
    Fermilab, Batavia, Illinois, USA
  • D.L. Bruhwiler, J.P. Edelen, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • A.H. Lumpkin, A. Zholents
    ANL, Lemont, Illinois, USA
  
  Funding: This work is supported by DOE grants DE-SC0017102, DE-SC0018559 and DE-SC0009914
In this paper we’ll describe the FAST-GREENS experimental program where a 4 m-long strongly tapered helical undulator with a seeded prebuncher is used in the high gain TESSA regime to convert a significant fraction (up to 10 %) of energy from the 240 MeV electron beam from the FAST linac to coherent 515 nm radiation. We’ll also discuss the longer term plans for the setup where by embedding the undulator in an optical cavity matched with the high repetition rate from the superconducting accelerator (3,9 MHz), a very high average power laser source can be obtained. Eventually, the laser pulses can be redirected onto the relativistic electrons to generate by inverse compton scattering a very high flux of circularly polarized gamma rays for polarized positron production. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT038  
About • Received ※ 09 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 12 June 2022 — Issue date ※ 02 July 2022
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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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