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Description
Bayesian optimization is an effective method for designing complex systems with costly, non-analytic black-box objective functions. It enables efficient exploration of the parameter space, making it well-suited for challenging problems in accelerator design that rely on computationally intensive simulations such as FLUKA.
This study presents a framework for applying Bayesian optimization techniques to the design of magnetic horns for muon-based accelerator facilities, including nuSTORM and the Muon Cooling Demonstrator. The optimization process spans a wide energy range, from 300 MeV/c to 6 GeV, covering both the low-energy regime relevant for ionization cooling channels and the higher-energy requirements of nuSTORM.
Batch sampling through specialized acquisition functions enables large-scale parallel simulations on a computational cluster. Leveraging surrogate models generated throughout the optimization, we identify refined horn geometries for the Muon Cooling Demonstrator and derive a minimal set of horn designs that efficiently cover the kinematic range of nuSTORM. These results demonstrate the versatility and scalability of Bayesian optimization for the design of next-generation muon-source systems.
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