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@unpublished{emma:ibic2021-thoa03,
author = {C. Emma and O.R. Convery and A.L. Edelen and M.J. Hogan},
title = {{Progress Towards Machine Learning-Based Real-Time Non-Destructive Prediction of the Longitudinal Phase Space Evolution in a Particle Accelerator}},
booktitle = {Proc. IBIC'21},
language = {english},
intype = {presented at the},
series = {International Beam Instrumentation Conference},
number = {10},
venue = {Pohang, Rep. of Korea},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2021},
note = {presented at IBIC'21 in Pohang, Rep. of Korea, unpublished},
abstract = {{We discuss progress towards the implementation of machine learning based real-time prediction of the Longitudinal Phase Space (LPS) evolution in a particle accelerator*. This work focuses specifically on the implementation of three separate longitudinal phase space virtual diagnostics at the FACET-II accelerator facility: one at the exit of the photoinjector, one after the second bunch compressor and one and the end of the linac before the experimental area. We present simulation results describing the simultaneous prediction of the LPS in a single-bunch and two-bunch mode of operation and discuss design choices for the machine learning architecture selected for the LPS prediction task**. Finally we discuss initial experimental deployment of the virtual diagnostics in regular accelerator operations.}},
}