The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - UNPB AU - Convery, O.R. AU - Gal, Y. AU - Hanuka, A. AU - Smith, L. ED - Kim, Changbum ED - Schaa, Volker R. W. ED - Kim, Dong-Eon ED - Lee, Jaeyu TI - Uncertainty Quantification and Robustness of Particle Accelerator Machine-Learning Based Models J2 - Proc. of IBIC2021, Pohang, Rep. of Korea, 24-28 May 2021 CY - Pohang, Rep. of Korea T2 - International Beam Instrumentation Conference T3 - 10 LA - english AB - Virtual Diagnostic (VD) is a computational tool built using deep learning that can be used to predict diagnostic outputs*. VDs are especially useful in systems where measuring outputs is invasive, limited, costly or runs the risk of modifying the output. In experiments with large ramifications, it is important to quantify the uncertainty of each prediction. Given out-of-distribution inputs (e.g. using the same machine in a different operation mode), it is also necessary to understand how robust the VD model is and how well it generalizes on unfamiliar data. In this work**, we use various compositions of neural networks to explore and enhance prediction uncertainty and robustness on data sets gathered from SLAC National Laboratory. We aim to accurately and confidently predict the longitudinal phase space images of electron beams. The ability to make informed decisions under uncertainty and limited computational power is crucial for reliable deployment of scalable deep learning tools on safety-critical systems such as particle accelerators. PB - JACoW Publishing CP - Geneva, Switzerland ER -