Document Type
Article
Publication Date
2025
DOI
10.1103/PhysRevAccelBeams.28.044603
Publication Title
Physical Review Accelerators and Beams
Volume
28
Issue
4
Pages
044603
Abstract
Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.
Rights
© 2025 The Authors.
Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Original Publication Citation
Goldenberg, S., Ahammed, K., Carpenter, A., Li, J., Suleiman, R., & Tennant, C. (2025). Data-driven gradient optimization for field emission management in a superconducting radio-frequency linac. Physical Review Accelerators and Beams, 28(4), Article 044603. https://doi.org/10.1103/PhysRevAccelBeams.28.044603
Repository Citation
Goldenberg, S.; Ahammed, K.; Carpenter, A.; Li, J.; Suleiman, R.; and Tennant, C., "Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac" (2025). Electrical & Computer Engineering Faculty Publications. 541.
https://digitalcommons.odu.edu/ece_fac_pubs/541
ORCID
0000-0003-0091-6986 (Li)
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons, Engineering Physics Commons