Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis


Review this paper

Submitted by hub-admin

Oct. 18, 2021, 7:46 a.m.

Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis

Jong T.A. de, Kok, D.N.L, Torren A.J.H. van der, Schopmans H., Tromp R.M., Molen S.J. van der & Jobst J.
Jong T.A. de, Kok, D.N.L, Torren A.J.H. van der, Schopmans H., Tromp R.M., Molen S.J. van der & Jobst J. (2019), Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis, Ultramicroscopy .
DOI:  None    


Number of reviews:   2

Brief Description
N/A
Why should we reproduce your paper?
Low Energy Electron Microscopy (LEEM) is a somewhat specific form of electron microscopy used to study surfaces and 2D materials. In this paper we describe a set of data processing techniques applied to LEEM and adapted to the peculiarities of LEEM. This is combined with a parallelized Python implementation using Dask in separate notebooks. So if you are interested in microscopy, image analysis, clustering of experimental physics data or parallel Python, this paper should be interesting to you.
What should reviewers focus on?

Resources

  Paper URL: https://doi.org/10.1016/j.ultramic.2019.112913
  Data URL: https://doi.org/10.4121/uuid:7f672638-66f6-4ec3-a16c-34181cc45202
  Code URL: https://doi.org/10.5281/zenodo.3539538
Tags: Python

Associated event

No associated event.