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RIS citation export for WEPV020: Learning to Lase: Machine Learning Prediction of FEL Beam Properties

AU  - Pollard, A.E.
AU  - Dunning, D.J.
AU  - Maheshwari, M.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Learning to Lase: Machine Learning Prediction of FEL Beam Properties
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 677
EP  - 680
KW  - network
KW  - diagnostics
KW  - simulation
KW  - electron
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEPV020
UR  - https://jacow.org/icalepcs2021/papers/wepv020.pdf
ER  -