17–22 May 2026
C.I.D
Europe/Zurich timezone

DETERMINISTIC METHODS AND BAYESIAN OPTIMIZATION ALGORITHMS APPLIED TO THE UH MĀNOA LINAC

MOP6318
18 May 2026, 16:00
2h
C.I.D

C.I.D

Deauville, France
Poster Presentation MC6.D13: Instrumentation: Artificial Intelligence Poster session

Speaker

Niels Bidault (University of Hawaiʻi at Mānoa)

Description

The University of Hawai‘I at Mānoa (UHM) linac delivers up to 45 MeV electron beams to a Free-Electron Laser(FEL) oscillator.
As the linac is being recommissioned for renewed FEL operation, we are developing simulation and optimization tools to recover operational settings and to explore the landscape of beam-manipulation techniques for future experimental apparatus.
This paper benchmarks classical deterministic methods and Bayesian optimization (BO) algorithms on three representative beam-optics tuning scenarios using a beam dynamics simulation model developed inhouse.
For problems with only a few free parameters, classical methods converge reliably, while finite-difference derivative information improves the performance of constrained
gradient-based solvers.
For the higher-dimensional case, BO with a Gaussian Process (GP) surrogate and SOBOL initialization provides a more robust path toward convergence.
The emphasis is on the number of optimization iterations required for each scenario, in order to anticipate the computational cost of applying the same workflow to higher-fidelity models.

Funding Agency

The U.S. Department of Energy, Office of the Science, under Contract No. DE-SC0025583

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Author

Niels Bidault (University of Hawaiʻi at Mānoa)

Co-authors

Dr Eremey Valetov (Michigan State University) Shek Hong Perseus Chan (University of Hawaiʻi at Mānoa) Siqi Li (University of Hawaii System) christian komo (University of Hawaiʻi at Mānoa)

Presentation materials

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