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RIS citation export for MOPOPT057: Updates in Efforts to Data Science Enabled MeV Ultrafast Electron Diffraction System

TY  - CONF
AU  - Biedron, S.
AU  - Babzien, M.
AU  - Bolin, T.B.
AU  - Fedurin, M.G.
AU  - Li, J.J.
AU  - Martin, D.
AU  - Martínez-Ramón, M.
AU  - Palmer, M.A.
AU  - Papka, M.E.
AU  - Sosa Guitron, S.I.
ED  - Zimmermann, Frank
ED  - Tanaka, Hitoshi
ED  - Sudmuang, Porntip
ED  - Klysubun, Prapong
ED  - Sunwong, Prapaiwan
ED  - Chanwattana, Thakonwat
ED  - Petit-Jean-Genaz, Christine
ED  - Schaa, Volker R.W.
TI  - Updates in Efforts to Data Science Enabled MeV Ultrafast Electron Diffraction System
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - MeV ultrafast electron diffraction (MUED) is a pump-probe characterization technique to study ultrafast phenomena in materials with high temporal and spatial resolution. This complex instrument can be advanced into a turn-key, high-throughput tool with the aid of machine learning (ML) mechanisms and high-performance computing. The MUED instrument at the Accelerator Test Facility in Brookhaven National Laboratory was employed to test different ML approaches for both data analysis and control. We characterized different materials using MUED, mainly polycrystalline gold and single crystal Ta2NiS5. Diffraction patterns were acquired in single shot mode and convolutional neural network autoenconder models were evaluated for noise reduction and the reconstruction error was studied to identify anomalous diffraction patterns. Electron beam energy jitter was analyzed from single shot diffraction patterns to be used as a novel diagnostic tool. The MUED beamline was also simulated using VSim to construct a surrogate model for control of beam shape and energy. Progress towards ML-based controls leveraging off Argonne Leadership Computing Facility resources will also be presented.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 397
EP  - 399
KW  - electron
KW  - network
KW  - gun
KW  - laser
KW  - experiment
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-MOPOPT057
UR  - https://jacow.org/ipac2022/papers/mopopt057.pdf
ER  -