CERN Accelerating science

Article
Report number	arXiv:2306.12844
Title	Combination of Measurement Data and Domain Knowledge for Simulation of Halbach Arrays With Bayesian Inference
Author(s)	Fleig, Luisa (CERN ; Darmstadt, Tech. U.) ; Liebsch, Melvin (CERN) ; Russenschuck, Stephan (CERN) ; Schöps, Sebastian (Darmstadt, Tech. U.)
Publication	2024-03
Imprint	2023-06-22
Number of pages	4
In:	IEEE Trans. Magn. 60 (2024) 7200104
DOI	10.1109/TMAG.2023.3301976
Subject category	cs.CE ; Computing and Computers
Accelerator/Facility, Experiment	CERN LHC ; FASER
Abstract	Accelerator magnets made from blocks of permanent magnets in a zero-clearance configuration are known as Halbach arrays. The objective of this work is the fusion of knowledge from different measurement sources (material and field) 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, by means of Bayesian inference, a posterior distribution. 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.
Copyright/License	publication: © 2023-2025 The Authors (License: CC-BY-4.0)

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