Speaker
Description
Uniform beam delivery is essential for irradiation experiments at the NASA Space Radiation Laboratory (NSRL) at BNL, where dose homogeneity directly impacts the fidelity of space-environment simulations. We present a new framework that couples BeamTracking.jl, a GPU/CPU-parallelized particle tracking code from the SciBmad project, with the Bayesian optimization package Xopt to predict and optimize beam uniformity in real time. BeamTracking.jl enables rapid evaluation of tens of thousands of macroparticles per iteration, providing statistically robust estimates of transverse fluence uniformity under varied optics settings. These predictions feed directly into Xopt, which constructs surrogate models of the beam response and proposes optimal machine configurations with minimal measurement overhead. We demonstrate this workflow on the NSRL beamline using a targeted set of quadrupole and steering magnet knobs. Simulations and online studies show that parallelized tracking reduces evaluation time by more than an order of magnitude, enabling Bayesian optimization loops on operationally relevant timescales. This work highlights the utility of BeamTracking.jl and Xopt as a combined toolset for rapid, data-driven tuning and automated uniformity optimization for future NSRL campaigns.
Funding Agency
Work funded by U.S. DOE grants DE-SC0025351 and DE-SC0024287.
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