Implementation of a spectrum fitting procedure using a robust peak model

Abstract

The need for an accurate description of characteristic x-ray lines has led to the development of complex peak models that combine a Gauss, a shelf and a tail function. Via relationships that describe the shelf and tail parameters as a function of the energy, it is possible to reduce the number of fit parameters significantly. In this work, we carried out an experiment to study the shelf and tail parameters obtained with an HPGe detector. We observed a strong discontinuity in the shelf and tail parameters around the K-edge energy of Ge. This indicates that these parameters are strongly dependent on the mass-attenuation coefficient of the detector material. Therefore, we propose relationships that describe those parameters as a function of the energy and the mass-attenuation coefficient. The implementation of these relationships in a fitting procedure results in a more robust fitting procedure with a much smaller number of fitting parameters. Excellent fits for HPGe spectra can be obtained. Because the detector dependence seems to be fairly general, we could successfully apply the model to Si(Li) spectra also.

Specific Ion Adsorption and Short-Range Interactions at the Air Aqueous Solution Interface

“…After energy calibration, the fluorescence spectra were analyzed following Ref. [23], using a Gaussian peak for each line with an energy dependent width, a shelf, and a tail accounting for detector related phenomena. The fluorescence of each element was determined by linear fitting, whereas the parameters of the width, tail, and shelf fraction were determined using a nonlinear Levenberg-Marquardt algorithm.…”

Quality Assurance Challenges in X-ray Emission Based Analyses, the Advantage of Digital Signal Processing

“…Our message differs from all previous investigators, as we declare that the signal processor DOES modify the spectra as confirmed by experiments. We recommend the reader look at the spectra presented in the literature [19][20][21][22][24][25][26] to see the large variations.…”

“…Since the tail is part of the spectrum it has to be accounted for, and it is a frequent exercise to determine parameter values used to describe these features. One of the latest is the paper of Van Gisel et al 22 They used a digital signal processor and a germanium detector. The spectra have a large amount of tailing reaching up to 100% of the peak area.…”

GIMPy: a software for the simulation of X‐ray fluorescence and reflectivity of layered materials

“…A wide list of parameters is needed for the definition of the detector, including the sensitive material, surface layers material and thickness, window material and thickness, electron-hole-pair creation energy, Fano factor, electronic noise, and specific parameters for the detector response function). Currently, two different algorithms have been implemented for the detector response function: the fully empirical approach by van Gysel et al [27] and the more physical one by Scholze et al [28] The calculation of pile-up intensities has also been implemented with a strategy following the description of Van Espen. [29] The geometric correction for the fluorescence signal is based on the one proposed by Beckhoff et al, [30] but it has been extended to all possible positioning of the detector on the plane of the beam.…”