NAPAC2022 - List of Authors (Diaz Cruz, J.A.)

Paper	Title	Page
THXD5	Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities
	J.A. Diaz Cruz, S. Biedron UNM-ECE, Albuquerque, USA J.A. Diaz Cruz SLAC, Menlo Park, California, USA
	The multiple systems involved in the operation of particle accelerators use diverse control systems to reach the desired operating point for the machine. Each system needs to tune several control parameters to achieve the required performance. Low-Level RF (LLRF) systems can be implemented using proportional-integral (PI) feedback loops, whose gains need to be optimized. In this paper, we explore Machine Learning (ML) as a tool to improve a traditional LLRF controller by tuning its gains using a Neural Network(NN).
	Slides THXD5 [0.970 MB]
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MOPA12	Commissioning of HOM Detectors in the First Cryomodule of the LCLS-II Linac	69
	J.A. Diaz Cruz UNM-ECE, Albuquerque, USA B.T. Jacobson, N.R. Neveu, J.P. Sikora SLAC, Menlo Park, California, USA
	Long-range wakefields (LRWs) may cause emittance dilution effects. LWRs are especially unwanted at facilities with low emittance beams like the LCLS-II at SLAC. Dipolar higher-order modes (HOMs) are a set of LRWs that are excited by off-axis beams. Two 4-channel HOM detectors were built to measure the beam-induced HOM signals for TESLA-type superconducting RF (SRF) cavities; they were tested at the Fermilab Accelerator Science and Technology (FAST) facility and are now installed at SLAC. The HOM detectors were designed to investigate LRW effects on the beam and to help with beam alignment. This paper presents preliminary results of HOM measurements at the first cryomodule (CM01) of the LCLS-II linac and describes the relevant hardware and setup of the experiment.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA12
About •	Received ※ 09 August 2022 — Accepted ※ 20 August 2022 — Issue date ※ 31 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

THXD5

Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities

J.A. Diaz Cruz, S. Biedron
UNM-ECE, Albuquerque, USA
J.A. Diaz Cruz
SLAC, Menlo Park, California, USA

The multiple systems involved in the operation of particle accelerators use diverse control systems to reach the desired operating point for the machine. Each system needs to tune several control parameters to achieve the required performance. Low-Level RF (LLRF) systems can be implemented using proportional-integral (PI) feedback loops, whose gains need to be optimized. In this paper, we explore Machine Learning (ML) as a tool to improve a traditional LLRF controller by tuning its gains using a Neural Network(NN).

Slides THXD5 [0.970 MB]

Cite •

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

MOPA12

Commissioning of HOM Detectors in the First Cryomodule of the LCLS-II Linac

69

J.A. Diaz Cruz
UNM-ECE, Albuquerque, USA
B.T. Jacobson, N.R. Neveu, J.P. Sikora
SLAC, Menlo Park, California, USA

Long-range wakefields (LRWs) may cause emittance dilution effects. LWRs are especially unwanted at facilities with low emittance beams like the LCLS-II at SLAC. Dipolar higher-order modes (HOMs) are a set of LRWs that are excited by off-axis beams. Two 4-channel HOM detectors were built to measure the beam-induced HOM signals for TESLA-type superconducting RF (SRF) cavities; they were tested at the Fermilab Accelerator Science and Technology (FAST) facility and are now installed at SLAC. The HOM detectors were designed to investigate LRW effects on the beam and to help with beam alignment. This paper presents preliminary results of HOM measurements at the first cryomodule (CM01) of the LCLS-II linac and describes the relevant hardware and setup of the experiment.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA12

About •

Received ※ 09 August 2022 — Accepted ※ 20 August 2022 — Issue date ※ 31 August 2022

Cite •

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