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RIS citation export for THPV043: Using AI for Management of Field Emission in SRF Linacs

AU  - Carpenter, A.
AU  - Degtiarenko, P.
AU  - Iftekharuddin, K.M.
AU  - Rahman, M.
AU  - Suleiman, R.
AU  - Tennant, C.
AU  - Turner, D.L.
AU  - Vidyaratne, L.S.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Using AI for Management of Field Emission in SRF Linacs
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting 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 operational 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 emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients 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 implementation of AI models will be deployed. Preliminary results and future plans are presented.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 970
EP  - 974
KW  - radiation
KW  - cavity
KW  - operation
KW  - detector
KW  - linac
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-THPV043
UR  - https://jacow.org/icalepcs2021/papers/thpv043.pdf
ER  -