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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14553 |
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| _version_ | 1866909598418468864 |
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| author | 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. |
| author_facet | 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. |
| contents | 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 (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. 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 SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Improved Current Density Reconstruction from 2D 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. Computational Physics Applied Physics Quantum Physics 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 (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. 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 SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources. |
| title | Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images |
| topic | Computational Physics Applied Physics Quantum Physics |
| url | https://arxiv.org/abs/2407.14553 |