Visualizing dispersive features in 2D image via minimum gradient method

Abstract

We developed a minimum gradient based method to track ridge features in 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, 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.

10 pages, 6 figures, 15 references

References in corpus (4)

• A precise method for visualizing dispersive features in image plots
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• Spin-Polarized Surface Resonances Accompanying Topological Surface State Formation
• Raman and fluorescence characteristics of resonant inelastic X-ray scattering from doped superconducting cuprates

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• Momentum-resolved electronic band structure and offsets in an epitaxial NbN/GaN superconductor/semiconductor heterojunction
• Three-Dimensional Fermi-Surface and Electron-Phonon Coupling in Semimetallic 1T-TiTe2 studied by Angle-Resolved Photoemission Spectroscopy
• Removing grid structure in angle-resolved photoemission spectra via deep learning method
• Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks
• About two-dimensional fits for the analysis of the scattering rates and renormalization functions from angle-resolved photoelectron spectroscopy data