{"paper":{"title":"Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"U-Net segmentation of magneto-optical images enables geometric tracking of magnetic stripe evolution and reveals two polarity-linked modes during annealing.","cross_cats":["cs.CV"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"B.S. Shivaram, Gia-Wei Chern, Hae Yong Kim, Kotaro Shimizu, Vin\\'icius Yu Okubo","submitted_at":"2025-09-15T00:23:23Z","abstract_excerpt":"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 skelet"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The U-Net model trained exclusively on synthetic degradations (additive white Gaussian and Simplex noise) produces segmentations of real experimental magneto-optical images that are sufficiently accurate and unbiased for downstream geometric measurements of length and curvature.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"U-Net segmentation of magneto-optical images combined with skeletonization and graph analysis quantifies the transition from quenched to annealed states in magnetic labyrinthine stripes and identifies two field-polarity-dependent evolution modes across 444 images.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"U-Net segmentation of magneto-optical images enables geometric tracking of magnetic stripe evolution and reveals two polarity-linked modes during annealing.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e1dfa22ddca75c8aac769d8237d152639032d92c71e7316a539bd5f65b6456c5"},"source":{"id":"2509.11485","kind":"arxiv","version":3},"verdict":{"id":"3e31a985-bbe8-4d79-9c07-bfc126a8b66f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T17:11:49.186154Z","strongest_claim":"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.","one_line_summary":"U-Net segmentation of magneto-optical images combined with skeletonization and graph analysis quantifies the transition from quenched to annealed states in magnetic labyrinthine stripes and identifies two field-polarity-dependent evolution modes across 444 images.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The U-Net model trained exclusively on synthetic degradations (additive white Gaussian and Simplex noise) produces segmentations of real experimental magneto-optical images that are sufficiently accurate and unbiased for downstream geometric measurements of length and curvature.","pith_extraction_headline":"U-Net segmentation of magneto-optical images enables geometric tracking of magnetic stripe evolution and reveals two polarity-linked modes during annealing."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.11485/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"77b88fbe75f53fbcd2668b557610ba988bd4173b341b48ef29caa00d8262ea0f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}