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RIS citation export for WEPA25: Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

AU  - Ahammed, K.
AU  - Carpenter, A.
AU  - Li, J.
AU  - Suleiman, R.
AU  - Tennant, C.
AU  - Vidyaratne, L.S.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 676
EP  - 678
KW  - cavity
KW  - radiation
KW  - detector
KW  - neutron
KW  - linac
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-WEPA25
UR  - https://jacow.org/napac2022/papers/wepa25.pdf
ER  -