Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

Reed, Niko R.; Bhutto, Danyal; Turner, Matthew J.; Daly, Declan M.; Oliver, Sean M.; Tang, Jiashen; Olsson, Kevin S.; Langellier, Nicholas; Ku, Mark J. H.; Rosen, Matthew S.; Walsworth, Ronald L.

doi:10.1103/PhysRevApplied.23.034035

Title: Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

Journal Article · 16 March 2025 · Physical Review Applied

DOI: https://doi.org/10.1103/PhysRevApplied.23.034035 · OSTI ID:2530991

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; Turner, Matthew J.;

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The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. Published by the American Physical Society 2025

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Sponsoring Organization:: USDOE

OSTI ID:: 2530991

Journal Information:: Physical Review Applied, Journal Name: Physical Review Applied Journal Issue: 3 Vol. 23; ISSN 2331-7019; ISSN PRAHB2

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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