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"

7 / 7 papers shown

Title
A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL Lynda Boukela Annika Eichler Julien Branlard N. Z. Jomhari 13 1 0 11 Jul 2024
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology Zhiyuan Chen Wei Lu Radhika Bhong Yimin Hu Brian Freeman Adam Carpenter 24 1 0 28 Jan 2024
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities Christoph Obermair T. Cartier-Michaud A. Apollonio W. Millar L. Felsberger ... W. Wuensch N. C. Lasheras M. Boronat Franz Pernkopf G. Burt 19 10 0 10 Feb 2022
Machine Learning in Nuclear Physics A. Boehnlein M. Diefenthaler C. Fanelli M. Hjorth-Jensen T. Horn ... M. Schram A. Scheinker Michael S. Smith Xin-Nian Wang Veronique Ziegler AI4CE 37 41 0 04 Dec 2021
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 32 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 21 25 0 25 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015