Fast Shimming Algorithm Based on Bayesian Optimization for Magnetic Resonance Based Dark Matter Search

The sensitivity and accessible mass range of magnetic resonance searches for axion-like dark matter depend on the homogeneity of applied magnetic fields. Optimizing homogeneity through shimming requires exploring a large parameter space, which can be prohibitively time consuming. The process of tuning the shim-coil currents has been automated by employing an algorithm based on Bayesian optimization. This method is especially suited for applications where the duration of a single optimization step prohibits exploring the parameter space extensively or when there is no prior information on the optimal operation point. Using the cosmic axion spin precession experiment-gradient low-field apparatus, it is shown that for the setup this method converges after approximate to 30 iterations to a sub-10 parts-per-million field homogeneity, which is desirable for our dark matter search. Axion-like particles (ALPs) are promising candidates for dark matter. The Cosmic Axion Spin Precession Experiment probes the interaction between ALPs and nuclear spins using magnetic resonance. For optimal sensitivity, correction fields (magnetic shims) are applied to decrease inhomogeneities over the spin sample. A novel approach based on Bayesian optimization is demonstrated to determine optimal shim settings in relatively few iterations.image

Automated summary

This article presents a method based on Bayesian optimization for automated tuning of magnetic field coil currents to optimize magnetic field homogeneity for improved sensitivity in magnetic resonance searches for axion-like dark matter. Experimental results demonstrate that this method converges to the desired sub-10 parts-per-million field homogeneity in a few iterations.