Machine Learning for Improved Current Density Reconstruction from NV-Diamond Magnetometry

Reconstructing current densities from magnetic field measurements is an important technique with applications in condensed matter, 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. We demonstrate a domain-transform manifold learning method that significantly exceeds the performance of analytic reconstructions for data with high noise or large standoff distances. This technique allows us to reduce the collection time of our NV-diamond magnetometer by a factor of about 400; and can also be useful in reconstructing weaker current sources.