Data-driven gradient optimization for field emission management in a superconducting radio-frequency linac
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 learnin...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-04-01
|
| Series: | Physical Review Accelerators and Beams |
| Online Access: | http://doi.org/10.1103/PhysRevAccelBeams.28.044603 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|