Author: Thomas, A.G.R.
Paper Title Page
MOPAB168 Nanoplasmonic Accelerators Towards Tens of TeraVolts per Meter Gradients Using Nanomaterials 574
 
  • A.A. Sahai, M. Golkowski, V. Harid
    CU Denver, Denver, Colorado, USA
  • C. Joshi
    UCLA, Los Angeles, California, USA
  • T.C. Katsouleas
    Duke ECE, Durham, North Carolina, USA
  • A. Latina, F. Zimmermann
    CERN, Geneva, Switzerland
  • J. Resta-López
    Cockcroft Institute, Warrington, Cheshire, United Kingdom
  • P. Taborek
    UCI, Irvine, California, USA
  • A.G.R. Thomas
    University of Michigan, Ann Arbor, Michigan, USA
  
  Funding: University of Colorado Denver
Ultra-high gradients which are critical for future advances in high-energy physics, have so far relied on plasma and dielectric accelerating structures. While bulk crystals were predicted to offer unparalleled TV/m gradients that are at least two orders of magnitude higher than gaseous plasmas, crystal-based acceleration has not been realized in practice. We have developed the concept of nanoplasmonic crunch-in surface modes which utilizes the tunability of collective oscillations in nanomaterials to open up unprecedented tens of TV/m gradients. Particle beams interacting with nanomaterials that have vacuum-like core regions, experience minimal disruptive effects such as filamentation and collisions, while the beam-driven crunch-in modes sustain tens of TV/m gradients. Moreover, as the effective apertures for transverse and longitudinal crunch-in wakes are different, the limitation of traditional scaling of structure wakefields to smaller dimensions is significantly relaxed. The SLAC FACET-II experiment of the nano2WA collaboration will utilize ultra-short, high-current electron beams to excite nonlinear plasmonic modes and demonstrate this possibility.
* doi:10.1109/ACCESS.2021.3070798
** doi:10.1142/S0217751X19430097
*** indico.fnal.gov/event/19478/contributions/52561
**** indico.cern.ch/event/867535/contributions/3716404 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB168  
About • paper received ※ 11 May 2021       paper accepted ※ 08 June 2021       issue date ※ 20 August 2021  
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THPAB349 Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control 4478
 
  • A. Aslam, M. Martínez-Ramón, S.D. Scott
    UNM-ECE, Albuquerque, USA
  • S. Biedron
    Argonne National Laboratory, Office of Naval Research Project, Argonne, Illinois, USA
  • S. Biedron
    Element Aero, Chicago, USA
  • S. Biedron
    UNM-ME, Albuquerque, New Mexico, USA
  • M. Burger, J. Murphy
    NERS-UM, Ann Arbor, Michigan, USA
  • K.M. Krushelnick, J. Nees, A.G.R. Thomas
    University of Michigan, Ann Arbor, Michigan, USA
  • Y. Ma
    IHEP, Beijing, People’s Republic of China
  • Y. Ma
    Michigan University, Ann Arbor, Michigan, USA
  
  Funding: Acknowledgements: 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 DE-SC0019468.
The applications of machine learning in today’s world encompass all fields of life and physical sciences. In this paper, we implement a machine learning based algorithm in the context of laser physics and particle accelerators. Specifically, a neural network-based optimisation algorithm has been developed that offers enhanced control over an ultrafast femtosecond laser in comparison to the traditional Proportional Integral and derivative (PID) controls. This research opens a new potential of utilising machine learning and even deep learning techniques to improve the performance of several different lasers and accelerators systems. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB349  
About • paper received ※ 20 May 2021       paper accepted ※ 02 July 2021       issue date ※ 17 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)