2016/12/23 by Yu He, Yan Wang, Zhi‐Xun Shen +1 · 1 citation
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computational physics #Computer science #Geology #Hydrocarbon exploration and reservoir analysis #Image (mathematics) #Machine Learning in Materials Science #Mathematics #Noise (video) #Nuclear Physics and Applications #Optics #Physics #Plot (graphics) #Ridge #Statistics #cond-mat.str-el #cond-mat.supr-con #physics.data-an
paper · pdf · doi:10.1063/1.4993919
published as Review of Scientific Instruments 88, 073903 (2017) · 10 pages, 6 figures, 15 references
arxiv created 2016/12/23 · openalex publication_date 2017/07/01 · arxiv updated 2018/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative application to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.