Using R for TL dating

Abstract

R is a programming language and environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques and is highly extensible [1]. Since 2012, a package specifically designed for luminescence dating is available [2]. However, it mainly includes functions for the analysis of OSL data.
Unlike OSL data, where the luminescence signal and background information are extracted from the same decay curve, for the analysis of TL data, different records have to be combined. Hence, data pretreatment is needed. Also, while OSL dating nearly exclusively uses the SAR protocol, in TL dating, the MAAD protocol is still applied as a standard. We therefore developed a series of R functions designed for the analysis of TL data.
The pretreatment of the TL data can be separated into three steps: First, separation of the TL curves used for the De estimation and the TL curves from preheat. Second, subtraction of the background signal from the luminescence signal. Third, alignment of the TL peaks. Peak scattering can be linked to different origins. If it is random, it is probably not linked to second-order kinetics and peak alignment will improve the De estimation.
For the De estimation, two functions using the SAR and MAAD protocol were developed. Both include plateau tests for each TL curve. The SAR function provides a De for each disc and functions from the R package ‘Luminescence’ are used to estimate a final De from the dose distribution. The MAAD function includes sublinearity correction and directly provides a final De estimate. In both cases, a series of parameters can be modified to improve the De assessment: (i) the integration temperature interval; (ii) the dose interval used; and (iii) the growth curve model. Rejection criteria are also included to identify problematic discs. Finally, the growth curve approach is combined with a dose plateau approach, which allows to improve the selection of the temperature interval.
One of the main problems encountered was tracking the uncertainties. Rather than estimating the uncertainties a posteriori, we generate an estimation of the error for each data point before any pretreatment. By considering random errors, it becomes possible to update this estimation each time the data are modified.
This project was realized in the context of the CRC 806 “Our way to Europe” funded by the German Research Foundation (DFG).

[1] R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/.
[2] Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M. (2012). Introducing an R package for luminescence dating analysis. Ancient TL 30: 1-8.

Resources

• Glasgow 2015 - David Strebler - Presentation FINAL.pdf

Bibliography

Strebler, D., Brill, D., Burow, C., Brückner, H. (2015): Using R for TL dating. UK Luminescence and ESR meeting 2015, Glasgow