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@inproceedings{gupta:ipac2021-wepab308,
author = {L. Gupta and A.L. Edelen and Y.K. Kim and C.E. Mayes and A.A. Mishra and N.R. Neveu},
title = {{Measurement-Based Surrogate Model of the SLAC LCLS-II Injector}},
booktitle = {Proc. IPAC'21},
pages = {3395--3398},
eid = {WEPAB308},
language = {english},
keywords = {laser, simulation, network, controls, cathode},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB308},
url = {https://jacow.org/ipac2021/papers/wepab308.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308},
abstract = {{There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here.}},
}