JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{pollard:icalepcs2021-wepv020,
author = {A.E. Pollard and D.J. Dunning and M. Maheshwari},
title = {{Learning to Lase: Machine Learning Prediction of FEL Beam Properties}},
booktitle = {Proc. ICALEPCS'21},
pages = {677--680},
eid = {WEPV020},
language = {english},
keywords = {network, diagnostics, simulation, FEL, electron},
venue = {Shanghai, China},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {18},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {03},
year = {2022},
issn = {2226-0358},
isbn = {978-3-95450-221-9},
doi = {10.18429/JACoW-ICALEPCS2021-WEPV020},
url = {https://jacow.org/icalepcs2021/papers/wepv020.pdf},
abstract = {{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.}},
}