IPAC2021 - List of Authors (Sosa, S.)

Paper	Title	Page
MOPAB290	Machine Learning-Based LLRF and Resonance Control of Superconducting Cavities	920
	J.A. Diaz Cruz, S. Biedron, M. Martínez-Ramón UNM-ECE, Albuquerque, USA J.A. Diaz Cruz SLAC, Menlo Park, California, USA R. Pirayesh UNM-ME, Albuquerque, New Mexico, USA S. Sosa ODU, Norfolk, Virginia, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468. Superconducting radio frequency (SRF) cavities with high loaded quality factors that operate in continuous wave (CW) and low beam loading are sensitive to microphonics-induced detuning. Cavity detuning can result in an increase of operational power and/or in a cavity quench. Such SRF cavities have bandwidths on the order of 10 Hz and detuning requirements can be as tight as 10 Hz. Passive methods to mitigate vibration sources and their impact in the cryomodule/cavity environment are widely used. Active resonance control techniques that use stepper motors and piezoelectric actuators to tune the cavity resonance frequency by compensating for microphonics detuning have been investigated. These control techniques could be further improved by applying Machine Learning (ML), which has shown promising results in other particle accelerator control systems. In this paper, we describe a Low-level RF (LLRF) and resonance control system based on ML methods that optimally and adaptively tunes the control parameters. We present simulations and test results obtained using a low power test bench with a cavity emulator.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB290
About •	paper received ※ 03 June 2021 paper accepted ※ 11 June 2021 issue date ※ 29 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Machine Learning-Based LLRF and Resonance Control of Superconducting Cavities

920

J.A. Diaz Cruz, S. Biedron, M. Martínez-Ramón
UNM-ECE, Albuquerque, USA
J.A. Diaz Cruz
SLAC, Menlo Park, California, USA
R. Pirayesh
UNM-ME, Albuquerque, New Mexico, USA
S. Sosa
ODU, Norfolk, Virginia, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468.
Superconducting radio frequency (SRF) cavities with high loaded quality factors that operate in continuous wave (CW) and low beam loading are sensitive to microphonics-induced detuning. Cavity detuning can result in an increase of operational power and/or in a cavity quench. Such SRF cavities have bandwidths on the order of 10 Hz and detuning requirements can be as tight as 10 Hz. Passive methods to mitigate vibration sources and their impact in the cryomodule/cavity environment are widely used. Active resonance control techniques that use stepper motors and piezoelectric actuators to tune the cavity resonance frequency by compensating for microphonics detuning have been investigated. These control techniques could be further improved by applying Machine Learning (ML), which has shown promising results in other particle accelerator control systems. In this paper, we describe a Low-level RF (LLRF) and resonance control system based on ML methods that optimally and adaptively tunes the control parameters. We present simulations and test results obtained using a low power test bench with a cavity emulator.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB290

About •

paper received ※ 03 June 2021 paper accepted ※ 11 June 2021 issue date ※ 29 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)