Abstract
We present a method of reducing noise in spectra that is based on eliminating low-order derivatives of reciprocal-space (RS) filter functions, yet ensuring that the functions roll off smoothly to minimize Gibbs oscillations. The approach takes advantage of the fact that information and noise are separated in RS. The method preserves as much information as possible, while reducing or even eliminating unwanted contributions (noise). To demonstrate the method we apply it to a model spectrum, data including an XPS spectrum of S2p in hierarchical NiCo2S4 nanosheets, and the Raman spectrum of 10-layer film of FePS3 with polarization direction of 90° with respect to the a-axis.
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Acknowledgments
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2019R1H1A2079786) and by the Development of Industrial Measuring Equipment Technology funded by the Korea Research Institute of Standards and Science (KRISS-2019-GP2019-0019).
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Le, V.L., Kim, T.J., Kim, Y.D. et al. Extended Gaussian Filtering for Noise Reduction in Spectral Analysis. J. Korean Phys. Soc. 77, 819–823 (2020). https://doi.org/10.3938/jkps.77.819
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DOI: https://doi.org/10.3938/jkps.77.819