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Title: Visualizing dispersive features in 2D image via minimum gradient method

Abstract

Here, 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.

Authors:
 [1]; ORCiD logo [2];  [1]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., Stanford, CA (United States)
  2. Stanford Univ., Stanford, CA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1390307
Alternate Identifier(s):
OSTI ID: 1372410
Grant/Contract Number:
AC02-76SF00515
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Review of Scientific Instruments
Additional Journal Information:
Journal Volume: 88; Journal Issue: 7; Journal ID: ISSN 0034-6748
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

He, Yu, Wang, Yan, and Shen, Zhi -Xun. Visualizing dispersive features in 2D image via minimum gradient method. United States: N. p., 2017. Web. doi:10.1063/1.4993919.
He, Yu, Wang, Yan, & Shen, Zhi -Xun. Visualizing dispersive features in 2D image via minimum gradient method. United States. doi:10.1063/1.4993919.
He, Yu, Wang, Yan, and Shen, Zhi -Xun. Mon . "Visualizing dispersive features in 2D image via minimum gradient method". United States. doi:10.1063/1.4993919.
@article{osti_1390307,
title = {Visualizing dispersive features in 2D image via minimum gradient method},
author = {He, Yu and Wang, Yan and Shen, Zhi -Xun},
abstractNote = {Here, 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.},
doi = {10.1063/1.4993919},
journal = {Review of Scientific Instruments},
number = 7,
volume = 88,
place = {United States},
year = {Mon Jul 24 00:00:00 EDT 2017},
month = {Mon Jul 24 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on July 24, 2018
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