Author: Abell, D.T.
Paper Title Page
MOPA50 Integrated Photonics Structure Cathodes for Longitudinally Shaped Bunch Trains 160
  • S.J. Coleman, D.T. Abell, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • R. Kapadia
    University of Southern California, Los Angeles, California, USA
  • S.S. Karkare
    Arizona State University, Tempe, USA
  • S.Y. Kim, P. Piot, J.F. Power
    ANL, Lemont, Illinois, USA
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DOE DE-SC0021681
Compact, high-gradient structure wakefield accelerators can operate at improved efficiency using shaped electron beams, such as a high transformer ratio beam shape, to drive the wakes. These shapes have generally come from a photocathode gun followed by a transverse mask to imprint a desired shape on the transverse distribution, and then an emittance exchanger (EEX) to convert that transverse shape into a longitudinal distribution. This process discards some large fraction of the beam, limiting wall-plug efficiency as well as leaving a solid object in the path of the beam. In this paper, we present a proposed method of using integrated photonics structures to control the emission pattern on the cathode surface. This transverse pattern is then converted into a longitudinal pattern at the end of an EEX. This removes the need for the mask, preserving the total charge produced at the cathode surface. We present simulations of an experimental set-up to demonstrate this concept at the Argonne Wakefield Accelerator.
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA50  
About • Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 03 October 2022
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MOPA55 Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry 164
  • J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown
    BNL, Upton, New York, USA
  • S. Calder
    ORNL RAD, Oak Ridge, Tennessee, USA
  • A.L. Edelen, B.D. O’Shea, R.J. Roussel
    SLAC, Menlo Park, California, USA
  • C.M. Hoffmann
    ORNL, Oak Ridge, Tennessee, USA
  • E.-C. Huang
    LANL, Los Alamos, New Mexico, USA
  • P. Piot
    Northern Illinois University, DeKalb, Illinois, USA
  • C. Tennant
    JLab, Newport News, Virginia, USA
  It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55  
About • Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
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MOPA57 Online Models for X-Ray Beamlines 170
  • B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov
    RadiaSoft LLC, Boulder, Colorado, USA
  • Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter
    BNL, Upton, New York, USA
  • N.B. Goldring
    STATE33 Inc., Portland, Oregon, USA
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593
X-ray beamlines transport synchrotron radiation from the magnetic source to the sample at a synchrotron light source. Alignment of elements such as mirrors and gratings are often done manually and can be quite time consuming. The use of photon beam models during operations is not common in the same way that they are used to great benefit for particle beams in accelerators. Linear and non-linear optics including the effects of coherence may be computed from source properties and augmented with measurements. In collaboration with NSLS-II, we are developing software tools and methods to include the model of the x-ray beam as it passes on its way to the sample. We are integrating the Blue-Sky beamline control toolkit with the Sirepo interface to several x-ray optics codes. Further, we are developing a simplified linear optics approach based on a Gauss-Schell model and linear canonical transforms as well as developing Machine Learning models for use directly from diagnostics data. We present progress on applying these ideas on NSLS-II beamlines and give a future outlook on this rather large and open domain for technological development.
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57  
About • Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 2022
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