Publications
Publication Information
| Title | Using AI for Management of Field Emission in SRF Linacs Poster |
| Authors | A. Carpenter, P. Degtiarenko, R. Suleiman, C. Tennant, D. Turner, L. Vidyaratne, K. Iftekharuddin, Md. Monibor Rahman |
| JLAB number | JLAB-ACO-21-3514 |
| LANL number | (None) |
| Other number | DOE/OR/23177-5389 |
| Document Type(s) | (Meeting) |
| Category: | Control Systems |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Meeting Poster compiled for ICALEPCS 2021 (18th International Conference on Accelerator and Large Experimental Physics Control Systems) | |
| Publication Abstract: | Field emission control, mitigation, and reduction is critical for reliable operation of high gradient supercon-ducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the opera-tional gradient, radiological environment via activated components, and reliability of CEBAF?s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emis-sion based on control system data such as radiation lev-els, cryogenic readbacks, and vacuum loads. The gradi-ents on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year?s operational program and initial implementa-tion of AI models will be deployed. Preliminary results and future plans are presented. |
| Experiment Numbers: | |
| Group: | Ops Admin Group |
| Document: | pptx |
| DOI: | |
| Accepted Manuscript: | |
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