IPAC2021 - List of Authors (Tao, 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)

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)

TUXC06	Visualizing Lattice Dynamic Behavior by Acquiring a Single Time-Resolved MeV	1311
	X. Yang, T.V. Shaftan, V.V. Smaluk, J. Tao, L. Wu, Y. Zhu BNL, Upton, New York, USA W. Wan ShanghaiTech University, Shanghai, People’s Republic of China
	We explore the possibility of visualizing the lattice dynamic behavior by acquiring a single time-resolved MeV UED image. Conventionally, multiple UED shots with varying time delays are needed to map out the entire dynamic process. The measurement precision is limited by the timing jitter between the pulses of laser pump and UED probe. We show that, by converting the longitudinal time of an electron bunch to the transverse position of a Bragg peak on the detector, one can obtain the full lattice dynamic process in a single electron pulse. We propose a novel design of a time-resolved UED with the capability of capturing a wide range of dynamic features in a single diffraction image. The work presented here is not only an extension of the ultrashort-pulse pump/long-pulse probe scheme being used in transient spectroscopy studies for decades but also advances the capabilities of MeV UED for future applications with tunable electron probe profile and detecting time range with femtosecond resolution. Furthermore, we present numerical simulations illustrating the capability of acquiring a single time-resolved diffraction image based on the case-by-case studies of lattice dynamic behavior.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUXC06
About •	paper received ※ 14 May 2021 paper accepted ※ 28 July 2021 issue date ※ 31 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)

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)

TUXC06

Visualizing Lattice Dynamic Behavior by Acquiring a Single Time-Resolved MeV

1311

X. Yang, T.V. Shaftan, V.V. Smaluk, J. Tao, L. Wu, Y. Zhu
BNL, Upton, New York, USA
W. Wan
ShanghaiTech University, Shanghai, People’s Republic of China

We explore the possibility of visualizing the lattice dynamic behavior by acquiring a single time-resolved MeV UED image. Conventionally, multiple UED shots with varying time delays are needed to map out the entire dynamic process. The measurement precision is limited by the timing jitter between the pulses of laser pump and UED probe. We show that, by converting the longitudinal time of an electron bunch to the transverse position of a Bragg peak on the detector, one can obtain the full lattice dynamic process in a single electron pulse. We propose a novel design of a time-resolved UED with the capability of capturing a wide range of dynamic features in a single diffraction image. The work presented here is not only an extension of the ultrashort-pulse pump/long-pulse probe scheme being used in transient spectroscopy studies for decades but also advances the capabilities of MeV UED for future applications with tunable electron probe profile and detecting time range with femtosecond resolution. Furthermore, we present numerical simulations illustrating the capability of acquiring a single time-resolved diffraction image based on the case-by-case studies of lattice dynamic behavior.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUXC06

About •

paper received ※ 14 May 2021 paper accepted ※ 28 July 2021 issue date ※ 31 August 2021

Export •

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