Abstract
AbstractSpatial inhomogeneity on the electronic structure is one of the vital keys to provide a better understanding of the emergent quantum phenomenon. Given the recent developments on spatially resolved ARPES (ARPES: angle-resolved photoemission spectroscopy), the information on the spatial inhomogeneity on the local electronic structure is now accessible. However, the next challenge becomes apparent as the conventional analysis encounters difficulty handling a large volume of a spatial mapping dataset, typically generated in the spatially resolved ARPES experiments. Here, we propose a machine-learning-based approach using unsupervised clustering algorithms (K-means and fuzzy-c-means) to examine the spatial mapping dataset. Our analysis methods enable automated categorization of the spatial mapping dataset with a much-reduced human intervention and workload, thereby allowing quick identification and visualization of the spatial inhomogeneity on the local electronic structures.
Funder
MEXT | Japan Society for the Promotion of Science
Funder: QST(National Institutes for Quantum Science and Technology) Grant Name: QST President’s Strategic Grant (QST Advanced Study Laboratory) Grant Reference Number: N/A
Publisher
Springer Science and Business Media LLC
Subject
Condensed Matter Physics,Electronic, Optical and Magnetic Materials
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献