Fast shimming algorithm based on Bayesian optimization for magnetic resonance based dark matter search

TL;DR

This paper introduces a Bayesian optimization-based algorithm to efficiently tune magnetic fields in dark matter searches, significantly reducing time and improving homogeneity for axion-like particle detection.

Contribution

The paper presents a novel automated shimming algorithm using Bayesian optimization tailored for magnetic resonance dark matter experiments.

Findings

Converges after ~30 iterations to sub-10 ppm homogeneity.

Reduces optimization time compared to traditional methods.

Enhances sensitivity of dark matter detection experiments.

Abstract

The sensitivity and accessible mass range of magnetic resonance searches for axionlike dark matter depends on the homogeneity of applied magnetic fields. Optimizing homogeneity through shimming requires exploring a large parameter space which can be prohibitively time consuming. We have automated the process of tuning the shim-coil currents 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 (CASPEr)-gradient low-field apparatus, we show that for our setup this method converges after approximately 30 iterations to a sub-10 parts-per-million field homogeneity which is desirable for our dark matter search.