Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators
Owen Convery, Lewis Smith, Yarin Gal, and Adi Hanuka

TL;DR
This paper explores methods to quantify uncertainty in deep learning-based virtual diagnostics for particle accelerators, enhancing the reliability of predictions in safety-critical applications.
Contribution
It introduces ensemble methods and quantile regression neural networks to assess prediction uncertainty in experimental accelerator data.
Findings
Ensemble methods improve uncertainty estimation accuracy.
Quantile regression neural networks provide reliable confidence intervals.
Enhanced uncertainty quantification aids safe decision-making in accelerator diagnostics.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
