TY - JOUR
T1 - Second derivative analysis and alternative data filters for multi-dimensional spectroscopies
T2 - A Fourier-space perspective
AU - Li, Rongjie
AU - Zhang, Xiaoni
AU - Miao, Lin
AU - Stewart, Luca
AU - Kotta, Erica
AU - Qian, Dong
AU - Kaznatcheev, Konstantine
AU - Sadowski, Jerzy T.
AU - Vescovo, Elio
AU - Alharbi, Abdullah
AU - Wu, Ting
AU - Taniguchi, Takashi
AU - Watanabe, Kenji
AU - Shahrjerdi, Davood
AU - Wray, L. Andrew
N1 - Funding Information:
This research used resources of the Center for Functional Nanomaterials and the National Synchrotron Light Source II, which are U.S. DOE Office of Science facilities at Brookhaven National Laboratory, under Contract No. DE-SC0012704. The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work at NYU was supported by the MRSEC Program of the National Science Foundation under Award Number DMR-1420073 . Growth of hexagonal boron nitride crystals by K.W. and T.T. was supported by the Elemental Strategy Initiative conducted by the MEXT, Japan and the CREST (JPMJCR15F3), JST.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.elspec.2019.05.001
DO - 10.1016/j.elspec.2019.05.001
M3 - Article
AN - SCOPUS:85065776839
VL - 238
JO - Journal of Electron Spectroscopy and Related Phenomena
JF - Journal of Electron Spectroscopy and Related Phenomena
SN - 0368-2048
M1 - 146852
ER -