Combination of Measurement Data and Domain Knowledge for Simulation of Halbach Arrays With Bayesian Inference

Accelerator magnets made from blocks of permanent magnets (PMs) in a zero-clearance configuration are known as Halbach arrays. The objective of this article is the fusion of knowledge from the magnetic field and material measurements and domain knowledge (magnetostatics) to obtain an updated magnet model of a Halbach array. From Helmholtz coil measurements of the magnetized blocks, a prior distribution of the magnetization is estimated. Measurements of the magnetic flux density are used to derive a posterior distribution by means of Bayesian inference. The method is validated on simulated data and applied to measurements of a dipole of the FASER detector. The updated magnet model of the FASER dipole describes the magnetic flux density one order of magnitude better than the prior magnet model.