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

Abstract

AbstractThe 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 ≈30 iterations to a sub‐10 parts‐per‐million field homogeneity, which is desirable for our dark matter search.