Towards multi-exponential analysis in optically stimulated luminescence

Abstract

Optically Stimulated Luminescence (OSL) data from quartz can follow different mathematical forms depending on the stimulation mode. These data can be described in terms of different multi-exponential models and can be numerically fitted using several well-known methods. Here we make a comparative analysis of the performance and stability of two models, the decay and peak form, and we consider different transformation methods for obtaining the peak form. For the numerical computations we use a nonlinear least squares (NLS) method and a method based on a first-kind Fredholm integral equation (FIE). Our analysis uses artificial data with three components (seven parameters including the background), and ten different levels of background, both the signal and the background contain Poisson distributed noise.Parameters derived using both models are acceptable (statistically consistent and on an average within ~1% of the expected value) and no obvious preference is observed for any particular model, although there may be a suggestion that peak form data shows a smaller mean bias. This conclusion seems to be independent of the type of peak transformations investigated here. Furthermore, it is found that transformation of OSL decay data to a peak form gives better results than direct measurement of peak form data by, for example, varying the stimulation light intensity. The comparison of the two numerical methods suggests that the NLS method performs somewhat better than the FIE method; however, the latter has the advantage that it does not require the 2 user's judgement on the number of components in the data. Testing of the NLS procedure on a measured quartz time-resolved OSL signal (TR-OSL) transformed into peak form yielded reliable parameter estimates even when the signal intensity was deliberately reduced by a factor of sixteen.

Charge recombination processes in minerals studied using optically stimulated luminescence and time-resolved exo-electrons

“…7b, OSL2 and OSE2). Ankjaergaard and Jain [7,8] We suggest that the fast decay in our TR-OSE signal is related to conduction band emptying after optical stimulation, whereas the more slowly decaying TR-OSE components are probably related to the thermal emptying of shallow traps. However, there are two possible alternative explanations for our data: it is possible that electron stimulation is a two stage process (Transition 3 in Fig.…”

What Is the Best Method to Fit Time-Resolved Data? A Comparison of the Residual Minimization and the Maximum Likelihood Techniques As Applied to Experimental Time-Correlated, Single-Photon Counting Data

“…Analyzing data via RM and ML methods has, of course, been previously discussed. ,− With a few exceptions, ,,, these analyses were limited to simulated data. Our work has been stimulated by the efforts of Maus et al, who provided a careful and detailed comparison of the RM (to which they refer as LS, “least squares”) and ML methods using experimental data.…”

Photon Counting Data Analysis: Application of the Maximum Likelihood and Related Methods for the Determination of Lifetimes in Mixtures of Rose Bengal and Rhodamine B

“…There are several comparisons of the ML and RM techniques, − but most of them have been limited to simulated data. In those cases where the techniques were applied to real experimental data, the comparisons were limited by several factors such as the exclusion of a real instrument response function (IRF), the bin size for the time channels of the histogram, the exclusion of a shift parameter that accounts for the wavelength difference between the instrument response function and the fluorescence signal, and, most importantly, not determining the minimum number of counts at which the respective techniques provide an acceptable result.…”