Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

11 June 2020

Papers citing "Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory"

4 / 4 papers shown

Title
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology Zhiyuan Chen Wei Lu Radhika Bhong Yimin Hu Brian Freeman Adam Carpenter 9 1 0 28 Jan 2024
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator S. Javed Pradeep Ramuhalli Arif Mahmood Yigit Yucesan Alexander Zhukov M. Schram Kishansingh Rajput Torri Jeske 6 15 0 22 Oct 2021
High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networks Jun Zhu Ye Chen F. Brinker W. Decking S. Tomin H. Schlarb AI4CE 17 25 0 25 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,134 0 06 Jun 2015