IPAC2021 - List of Authors (Li, J.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
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)