Systematic study of background subtraction techniques for EELS

Quantification of electron energy-loss spectra by background subtraction is usually done by fitting an inverse power-law (AE^-r) function to the pre-edge region. The errors associated with background fit and extrapolation have been discussed by Egerton in terms of so-called h-parameters. More sophisticated methods such as multiple linear least square fits have been implemented in software packages such as Hyperspy, EELSMODEL and Digital Micrograph. In background subtraction, there is always a trade-off between systematic and statistical errors in quantification of core-losses. In some cases either due to noise, near edge or extended fine structures in preceding edges, the extrapolated background can cross the spectrum, which leads to large systematic underestimate of the core-loss intensity. Background subtraction techniques with exponential fitting can be explored more systematically and a new approach on how the quantification can be improved by choosing different functions to fit in pre-edge regions will be discussed. In particular, modelled pre-edge backgrounds can be forced to not cross the spectrum by introducing a linear offset function, thereby minimizing the underestimate of the core-loss. The precision of EELS quantification with respect to spectrometer entrance aperture has been discussed by Bertoni & Verbeeck. Modelling the background can also be explored more extensively by fitting an inverse power-law or exponential fit to the post-ionisation edge and shifting the background curve fitted downwards to pass though the edge onset. This lead to an overestimate of the core-loss. The best background fit and its reliability can be calculated from the error bars associated with the under and over-estimated intensities.

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Recommended citation: V. C. Angadi and T. Walther (2016), "Systematic study of background subtraction techniques for EELS", In 2016 Electron Microscopy and Analysis Group Conference, Durham, UK.