  Virtual Diagnostic (VD) is a computational tool based on deep learning that
can be used to predict a diagnostic output. VDs are especially useful in
systems where measuring the output is invasive, limited, costly or runs the
risk of altering the output. Given a prediction, it is necessary to relay how
reliable that prediction is, i.e. quantify the uncertainty of the prediction.
In this paper, we use ensemble methods and quantile regression neural networks
to explore different ways of creating and analyzing prediction's uncertainty on
experimental data from the Linac Coherent Light Source at SLAC National Lab. We
aim to accurately and confidently predict the current profile or longitudinal
phase space images of the electron beam. The ability to make informed decisions
under uncertainty is crucial for reliable deployment of deep learning tools on
safety-critical systems as particle accelerators.
