Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning
- Old Dominion Univ., Norfolk, VA (United States)
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
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.
- Research Organization:
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Nuclear Physics (NP)
- DOE Contract Number:
- AC05-06OR23177
- OSTI ID:
- 2204705
- Report Number(s):
- JLAB-ACC-22-3806; DOE/OR/23177-7274
- Resource Relation:
- Conference: NAPAC 2022, Albuquerque, NM, USA, 07-12 August 2022
- Country of Publication:
- United States
- Language:
- English
Similar Records
TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES
TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES