Decoding magnetic texture

Abstract

In magnetically ordered materials, magnetic field and temperature variations modify the magnetic texture through their coupling to the local energy landscape, imprinting distinct fingerprints in the resulting magnetic domain patterns. Retrieving these conditions from the pattern remains challenging, as stochastic nucleation and hysteresis produce a nonlinear, multivariate, and ambiguous relationship between magnetic domain morphology and external stimuli. To decode these fingerprints, we designed a controlled magneto-optical inference experiment that reconstructs magnetic field, temperature, and magnetic history from a single fine-scale, high-contrast, pixel-resolved optical polarization map of feature-rich magnetic domain textures in a bismuth-substituted yttrium iron garnet film. Deep convolutional neural networks are complemented by feature-based neural-network inference using hand-crafted, physically interpretable descriptors of measured magneto-optical image data, linking the decoded information to material-dependent features and exploring their contributions. Together, these results establish magnetic texture as a high-fidelity record of external conditions enabling accurate single image multiparametric sensing and paving the way for data-driven explorations of complex magnetic states. Uncovering the physically interpretable features that encode this record sheds new light on the physics of magnetic domain formation.