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arXiv · 2509.11485

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation

Vinícius Yu Okubo·Kotaro Shimizu·B. S. Shivaram·Gia-Wei Chern·Hae Yong Kim
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Abstract

Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization challenging. We investigate the evolution of such patterns in bismuth-doped yttrium iron garnet (Bi:YIG) films subjected to a magnetic field annealing protocol. A U-Net deep learning model, trained with synthetic degradations including additive white Gaussian and Simplex noise, enables robust segmentation of experimental magneto-optical images despite noise and occlusions. Building on this segmentation, we develop a geometric analysis pipeline based on skeletonization, graph mapping, and spline fitting, which quantifies local stripe propagation through length and curvature measurements. Applying this framework to 444 images from 12 annealing protocol trials, we analyze the transition from the "quenched" state to a more parallel and coherent "annealed" state, and identify two distinct evolution modes (Type A and Type B) linked to field polarity. Our results provide a quantitative analysis of geometric and topological properties in magnetic stripe patterns and offer new insights into their local structural evolution, and establish a general tool for analyzing complex labyrinthine systems.

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Vinícius Yu Okubo, Kotaro Shimizu, B. S. Shivaram, Gia-Wei Chern, Hae Yong Kim. 2026-06-08. Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation. https://arxiv.org/abs/2509.11485

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