Bayesian 2D Current Reconstruction from Magnetic Images

We employ a Bayesian image reconstruction scheme to recover 2D currents from magnetic flux imaged with scanning SQUIDs (Superconducting Quantum Interferometric Devices). Magnetic flux imaging is a versatile tool to locally probe currents and magnetic moments, however present reconstruction methods sacrifice resolution due to numerical instability. Using state-of-the-art blind deconvolution techniques we recover the currents, point-spread function and height of the SQUID loop by optimizing the probability of measuring an image. We obtain uncertainties on these quantities by sampling reconstructions. This generative modeling technique could be used to develop calibration protocols for scanning SQUIDs, to diagnose systematic noise in the imaging process, and can be applied to many tools beyond scanning SQUIDs.