Second derivative analysis and alternative data filters for multi-dimensional spectroscopies: a Fourier-space perspective
Data Analysis, Statistics and Probability
2019-06-25 v1 Materials Science
Abstract
The second derivative image (SDI) method is widely applied to sharpen dispersive data features in multi-dimensional spectroscopies such as angle resolved photoemission spectroscopy (ARPES). Here, the SDI function is represented in Fourier space, where it has the form of a multi-band pass filter. The interplay of the SDI procedure with undesirable noise and background features in ARPES data sets is reviewed, and it is shown that final image quality can be improved by eliminating higher Fourier harmonics of the SDI filter. We then discuss extensions of SDI-like band pass filters to higher dimensional data sets, and how one can create even more effective filters with some a priori knowledge of the spectral features.
Keywords
Cite
@article{arxiv.1906.09608,
title = {Second derivative analysis and alternative data filters for multi-dimensional spectroscopies: a Fourier-space perspective},
author = {Rongjie Li and Xiaoni Zhang and Lin Miao and Luca Stewart and Erica Kotta and Dong Qian and Konstantine Kaznatcheev and Jerzy T. Sadowski and Elio Vescovo and Abdullah Alharbi and Ting Wu and Takashi Taniguchi and Kenji Watanabe and Davood Shahrjerdi and L. Andrew Wray},
journal= {arXiv preprint arXiv:1906.09608},
year = {2019}
}
Comments
7 pages, 5 figures