Document Type

Conference Paper

Publication Date

2022

DOI

10.18429/JACoW-NAPAC2022-WEPA25

Publication Title

Proceedings of the North American Particle Accelerator Conference

Pages

676-678

Conference Name

Proceedings of the North American Particle Accelerator Conference

Abstract

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.

Rights

Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.

Original Publication Citation

Ahammed, K., Li, J., Carpenter, A., Suleiman, R., Tennant, C., & Vidyaratne, L. S. (2022). Field emission migration in CEBAF SRF cavities using deep learning. In S. Biedron, E. Simakov, S. Milton, P. M. Anisimov, V. R. W. Schaa (Eds.), Proceedings of the North American Particle Accelerator Conference (pp. 676-678). JACOW Publishing. https://doi.org/10.18429/JACoW-NAPAC2022-WEPA25

ORCID

0000-0003-0091-6986 (Li), 0000-0001-7125-5703 (Ahammed)

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