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https://doi.org/10.18429/JACoW-NAPAC2022-TUPA41
Title Applications of Machine Learning in Photo-Cathode Injectors
Authors
  • A. Aslam
    UNM-ECE, Albuquerque, USA
  • M. Babzien
    BNL, Upton, New York, USA
  • S. Biedron
    Element Aero, Chicago, USA
Abstract To configure a photoinjector to reproduce a given electron bunch with the desired characteristics, it is necessary to adjust the operating parameters with high precision. More or less, the fine tunability of the laser parameters are of extreme importance as we try to model further applications of the photoinjector. The laser pulse incident on the photocathode critically affects the electron bunch 3D phase space. Parameters such as the laser pulse transverse shape, total energy, and temporal profile must be controlled independently, any laser pulse variation over both short and long-time scales also requires correction. The ability to produce arbitrary laser intensity distributions enables better control of electron bunch transverse and longitudinal emittance by affecting the space-charge forces throughout the bunch. In an accelerator employing a photoinjector, electron optics in the beamline downstream are used to transport, manipulate, and characterize the electron bunch. The adjustment of the electron optics to achieve a desired electron bunch at the interaction point is a much better understood problem than laser adjustment, so this research emphasizes laser shaping.
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Conference NAPAC2022
Series International Particle Accelerator Conference (5th)
Location Albuquerque, NM, USA
Date 07-12 August 2022
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Sandra Biedron (Element Aero, Chicago, IL, USA); Evgenya Simakov (LANL, Los Alamos, NM, USA); Stephen Milton (Element Aero, Chicago, IL, USA); Petr M. Anisimov (LANL, Los Alamos, NM, USA); Volker R.W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-232-5
Online ISSN 2673-7000
Received 30 July 2022
Revised 12 August 2022
Accepted 13 August 2022
Issue Date 07 September 2022
DOI doi:10.18429/JACoW-NAPAC2022-TUPA41
Pages 441-442
Copyright
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.