Document Type
Conference Paper
Publication Date
2022
DOI
10.18429/JACoW-NAPAC2022-WEPA25
Publication Title
Proceedings of the North American Particle Accelerator Conference
Pages
676-678
Conference Name
Proceedings of the North American Particle Accelerator Conference
Abstract
The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.
Rights
Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.
Original Publication Citation
Ahammed, K., Li, J., Carpenter, A., Suleiman, R., Tennant, C., & Vidyaratne, L. S. (2022). Field emission migration in CEBAF SRF cavities using deep learning. In S. Biedron, E. Simakov, S. Milton, P. M. Anisimov, V. R. W. Schaa (Eds.), Proceedings of the North American Particle Accelerator Conference (pp. 676-678). JACOW Publishing. https://doi.org/10.18429/JACoW-NAPAC2022-WEPA25
Repository Citation
Ahammed, K.; Li, J.; Carpenter, A.; Suleiman, R.; Tennant, C.; and Vidyaratne, L., "Field Emission Migration in CEBAF SRF Cavities Using Deep Learning" (2022). Electrical & Computer Engineering Faculty Publications. 494.
https://digitalcommons.odu.edu/ece_fac_pubs/494
ORCID
0000-0003-0091-6986 (Li), 0000-0001-7125-5703 (Ahammed)