Author: Monk, D.J.
Paper Title Page
MOPAB286 Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System 906
 
  • M.A. Fazio, S. Biedron, M. Martínez-Ramón, D.J. Monk, S.I. Sosa Guitron
    UNM-ECE, Albuquerque, USA
  • M. Babzien, K.A. Brown, M.G. Fedurin, J.J. Li, M.A. Palmer, J. Tao
    BNL, Upton, New York, USA
  • S. Biedron, T. Talbott
    UNM-ME, Albuquerque, New Mexico, USA
  • J. Chen, A.J. Hurd, N.A. Moody, R. Prasankumar, C. Sweeney
    LANL, Los Alamos, New Mexico, USA
  • D. Martin, M.E. Papka
    ANL, Lemont, Illinois, USA
  
  Funding: US DOE, SC, BES, MSE, award DE-SC0021365 and DOE NNSA award 89233218CNA000001 through DOE’s EPSCoR program in Office of BES with resources of DOE SC User Facilities BNL’s ATF and ALCF.
A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286  
About • paper received ※ 20 May 2021       paper accepted ※ 09 June 2021       issue date ※ 25 August 2021  
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MOPAB314 Surrogate Modeling for MUED with Neural Networks 970
 
  • D.J. Monk, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron
    UNM-ECE, Albuquerque, USA
  • M. Babzien, K.A. Brown, M.A. Palmer, J. Tao
    BNL, Upton, New York, USA
  • D. Martin, M.E. Papka
    ANL, Lemont, Illinois, USA
  • T. Talbott
    UNM-ME, Albuquerque, New Mexico, USA
  
  Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314  
About • paper received ※ 20 May 2021       paper accepted ※ 07 June 2021       issue date ※ 15 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)