Universal Reconstruction of Complex Magnetic Profiles with Minimum Prior Assumptions

TL;DR

This paper introduces a GPU-accelerated method for reconstructing complex magnetic profiles from measured magnetic fields with minimal prior assumptions, validated through simulations and real experiments on diverse magnetic structures.

Contribution

The authors develop a universal, efficient reconstruction technique that handles complex magnetic structures with minimal prior knowledge, suitable for quantum sensing applications.

Findings

Successfully reconstructs magnetic domains in basalt consistent with paleomagnetic data.

Reveals hexagonal magnetic Moire patterns in twisted CrI3.

Demonstrates robustness under realistic experimental noise and resolution constraints.

Abstract

Understanding intricate magnetic structures in materials is essential for advancing materials science, spintronics, and geology. Recent developments of quantum-enabled magnetometers, such as nitrogen-vacancy (NV) centers in diamond, have enabled direct imaging of magnetic field distributions across a wide range of magnetic profiles. However, reconstructing the magnetization from an experimentally measured magnetic field map is a complex inverse problem, further complicated by measurement noise, finite spatial resolution, and variations in sample-to-sensor distance. In this work, we present a novel and efficient GPU-accelerated method for reconstructing spatially varying magnetization density from measured magnetic fields with minimal prior assumptions. We validate our method by simulating diverse magnetic structures under realistic experimental conditions, including multi-domain ferromagnetism and magnetic spin textures such as skyrmion, anti-skyrmion, and meron. Experimentally, we reconstruct the magnetization of a micrometer-scale Apollo lunar mare basalt (sample 10003,184) and a nanometer-scale twisted double-trilayer CrI3. The basalt exhibits soft ferromagnetic domains consistent with previous paleomagnetic studies, whereas the CrI3 system reveals a well-defined hexagonal magnetic Moire superlattice. Our approach provides a versatile and universal tool for investigating complex magnetization profiles, paving the way for future quantum sensing experiments.