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)