IPAC2021 - List of Authors (Mishra, A.A.)

Paper	Title	Page
WEPAB308	Measurement-Based Surrogate Model of the SLAC LCLS-II Injector	3395
	L. Gupta, Y.K. Kim University of Chicago, Chicago, Illinois, USA A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu SLAC, Menlo Park, California, USA
	Funding: This project was funded by the DOE SCGSR Program. 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.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308
About •	paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Measurement-Based Surrogate Model of the SLAC LCLS-II Injector

3395

L. Gupta, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu
SLAC, Menlo Park, California, USA

Funding: This project was funded by the DOE SCGSR Program.
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.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308

About •

paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021

Export •

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