Abstract
Background removal is an important step in the quantitative analysis of electron energy-loss structure. Existing methods usually require an energy-loss region outside the fine structure in order to estimate the background. This paper describes a method for signal-from-background separation that is based on subspace division. The linear space is divided into two subspaces. The signal is recovered from a linear subspace containing no background information, and the other subspace containing the background is discarded. This method does not rely on any signal outside the energy-loss range of interest and should be very helpful for multiple linear least-squares (MLLS) regression analysis on experimental signals with little or no available smooth pre-edge region or with overlapping pre-edge features. Use of the algorithm is demonstrated with several practical applications, including closely overlapping core-loss spectra and zero-loss peak removal. Tests based on experimental data indicate that the algorithm has similar or better performance relative to conventional pre-edge power-law fitting methods in applications such as MLLS regression for electron energy-loss near-edge structure.
Original language | English (US) |
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Pages (from-to) | 25-31 |
Number of pages | 7 |
Journal | Ultramicroscopy |
Volume | 195 |
DOIs | |
State | Published - Dec 2018 |
Keywords
- Background subtraction
- EELS
- Fine structure
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Instrumentation
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DM script for EELS background removal based on subspace division
Lu, S. (Creator), Kormondy, K. J. (Contributor) & Smith, D. (Contributor), Mendeley Data, Nov 2 2018
DOI: 10.17632/r74szh8f69.1, https://data.mendeley.com/datasets/r74szh8f69
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