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@unpublished{hanuka:ipac2021-frxc07,
author = {A. Hanuka and O.R. Convery and Y. Gal and L. Smith},
title = {{Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators}},
booktitle = {Proc. IPAC'21},
language = {english},
intype = {presented at the},
series = {International Particle Accelerator Conference},
number = {12},
venue = {Campinas, SP, Brazil},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
note = {presented at IPAC'21 in Campinas, SP, Brazil, unpublished},
abstract = {{Current diagnostic tools for characterizing a system are often costly, limited and invasive, i.e. interrupt the system’s normal operation. A Virtual Diagnostic (VD) is a computational tool based on deep learning that can be used to predict the diagnostic output. For practical usage of VDs, it is necessary to quantify the prediction’s reliability, namely the uncertainty in that prediction. In this paper, we applied an ensemble of neural networks to create uncertainty and explore various ways of analyzing prediction’s uncertainty using experimental data from the Linac Coherent Light Source particle accelerator at SLAC National Laboratory. We aim to accurately and confidently predict the longitudinal properties of the electron beam as given by their phase-space images. The ability to make informed decisions under uncertainty is crucial for reliable deployment of deep learning tools on safety-critical systems as particle accelerators.}},
}