| Paper | Title | Page |
|---|---|---|
| MOPOMS029 | HPC Modeling of a High-Gradient C-Band Linac for Hard X-Ray Free-Electron Lasers | 703 |
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The production of soft to hard x-rays (up to 25 keV) at XFEL (x-ray free-electron laser) facilities has enabled new developments in a broad range of disciplines. Great potential exists for new scientific discovery at higher energies (42+ keV) such as envisioned at MaRIE (Matter-Radiation Interactions in Extremes) at Los Alamos National Laboratory. These instruments can require a large amount of real estate, which quickly escalates costs: The driver of the FEL is typically an electron beam linear accelerator (LINAC) and the need for higher beam energies capable of generating these X-rays can dictate that the linac becomes longer. State of art accelerating technology is required to reduce the linac length by reducing the size of the cavities, providing for compact, high-frequency, high acceleration gradients. Here, we describe using the Argonne Leadership Computing Facility (ALCF) to facilitate our investigations into design concepts for future XFEL high-gradient LINAC’s in the C-band (~4-8 GHz). We investigate two different traveling wave (TW) geometries optimized for high-gradient operation as modeled at the ALCF using VSim software.*
* https://www.txcorp.com |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS029 | |
| About • | Received ※ 03 July 2022 — Accepted ※ 04 July 2022 — Issue date ※ 08 July 2022 | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |
| TUPOST027 | Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities | 915 |
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| 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. Traditional Low-Level RF (LLRF) systems are implemented as proportional-integral 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). We present the data production scheme and a control parameter optimization using a NN. The NN training is performed using the THETA supercomputer. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST027 | |
| About • | Received ※ 14 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 20 June 2022 | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |