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説明:

Spatially-resolved ARPES is a powerful tool for probing local electronic structures in low-dimensional materials, but its analysis becomes challenging for spatially inhomogeneous samples due to spectral variations, feature overlap, and minor shifts. Here, we present a practical framework based on non-negative matrix factorization (NMF), which decomposes ARPES spectra into physically interpretable components without relying on prior assumptions. Visualizing the activation matrix as spatial heatmaps reveals latent spectral structures and provides an intuitive map of how individual components are distributed, enabling identification of the domains and local electronic variations. We validate this framework using epitaxial graphene on SiC, demonstrating its ability to quantitatively disentangle spectral features associated with layer thickness, step structures, and growth conditions. This study establishes the NMF-based framework as a scalable and robust tool for managing large-scale datasets and assessing electronic inhomogeneity in low-dimensional materials.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Photoemission spectroscopy, non-negative matrix factorization, graphene, machine learning, data-driven analysis

刊行年月日: 2026-06-19

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 6 2688747

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6365

公開URL: https://doi.org/10.1080/27660400.2026.2688747

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更新時刻: 2026-06-24 15:38:41 +0900

MDRでの公開時刻: 2026-06-24 18:27:28 +0900