Author: Shang, H.
Paper Title Page
TUPA23 First Beam Results Using the 10-kW Harmonic Rf Solid-State Amplifier for the APS Particle Accumulator Ring 398
  • K.C. Harkay, T.G. Berenc, J.R. Calvey, J.C. Dooling, H. Shang, T.L. Smith, Y. Sun, U. Wienands
    ANL, Lemont, Illinois, USA
  Funding: Work supported by U. S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357.
The Advanced Photon Source (APS) particle accumulator ring (PAR) was designed to accumulate linac pulses into a single bunch using a fundamental radio frequency (rf) system, and longitudinally compress the beam using a harmonic rf system prior to injection into the booster. The APS Upgrade injectors will need to supply full-current bunch replacement with high single-bunch charge for swap-out injection in the new storage ring. Significant bunch lengthening is observed in the PAR at high charge, which negatively affects beam capture in the booster. Predictions showed that the bunch length could be compressed to better match the booster acceptance using a combination of higher beam energy and higher harmonic gap voltage. A new 10-kW harmonic rf solid-state amplifier (SSA) was installed in 2021 to raise the gap voltage and improve bunch compression. The SSA has been operating reliably. Initial results show that the charge-dependent bunch lengthening in PAR with higher gap voltage agrees qualitatively with predictions. A tool was written to automate bunch length data acquisition. Future plans to increase the beam energy, which makes the SSA more effective, will also be summarized.
poster icon Poster TUPA23 [2.477 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA23  
About • Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 07 October 2022
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THXD4 Online Accelerator Tuning with Adaptive Bayesian Optimization 842
  • N. Kuklev, M. Borland, G.I. Fystro, H. Shang, Y. Sun
    ANL, Lemont, Illinois, USA
  Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
Particle accelerators require continuous adjustment to maintain beam quality. At the Advanced Photon Source (APS) this is accomplished using a mix of operator-controlled and automated tools. To improve the latter, we explored the use of machine learning (ML) at the APS injector complex. The core approach we chose was Bayesian optimization (BO), which is well suited for sparse data tasks. To enable long-term online use, we modified BO into adaptive Bayesian optimization (ABO) though auxiliary models of device drift, physics-informed quality and constraint weights, time-biased data subsampling, digital twin retraining, and other approaches. ABO allowed for compensation of changes in inputs and objectives without discarding previous data. Benchmarks showed better ABO performance in several simulated and experimental cases. To integrate ABO into the operational workflow, we developed a Python command line utility, pysddsoptimize, that is compatible with existing Tcl/Tk tools and the SDDS data format. This allowed for fast implementation, debugging, and benchmarking. Our results are an encouraging step for the wider adoption of ML at APS.
slides icon Slides THXD4 [4.797 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THXD4  
About • Received ※ 01 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 08 October 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)