Convexity constraints on linear background models for electron energy-loss spectra

TL;DR

This paper introduces convexity constraints for linear background models in electron energy-loss spectra, enhancing accuracy and computational efficiency for large-scale, unsupervised elemental analysis.

Contribution

It develops a convexity-constrained linear background model for EELS that outperforms traditional methods and enables fast, guaranteed solutions via quadratic programming.

Findings

Model outperforms power-law backgrounds on experimental and simulated data.

Constraints allow for fast, quadratic programming-based fitting.

Improves elemental quantification in wide energy ranges.

Abstract

In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simulated backgrounds, especially for wide energy ranges, and thus improves elemental quantification results. Owing to the model's linearity, the constraints can be imposed through fitting by quadratic programming. This has important advantages over conventional nonlinear power-law fitting such as high speed and a guaranteed unique solution without need for initial parameters. As such, the need for user input is significantly reduced, which is essential for unsupervised treatment of large data sets. This is demonstrated on a demanding spectrum image of a semiconductor device sample with a high number of elements over a wide energy range.