Preprints
https://doi.org/10.5194/gchron-2020-40
https://doi.org/10.5194/gchron-2020-40

  03 Dec 2020

03 Dec 2020

Review status: a revised version of this preprint was accepted for the journal GChron and is expected to appear here in due course.

Towards an improvement of OSL age uncertainties: modelling OSL ages with systematic errors, stratigraphic constraints and radiocarbon ages using the R package BayLum

Guillaume Guérin1,2, Christelle Lahaye1, Maryam Heydari1, Martin Autzen3,4, Jan-Pieter Buylaert3,4, Pierre Guibert1, Mayank Jain3, Sebastian Kreutzer5,1, Andrew S. Murray4, Kristina J. Thomsen3, Petra Urbanova1, and Anne Philippe6 Guillaume Guérin et al.
  • 1UMR 5060 CNRS - Université Bordeaux Montaigne, IRAMAT-CRP2A, Maison de l’archéologie, Esplanade des Antilles, 33607 Pessac cedex, France
  • 2Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000 Rennes, France
  • 3Center for Nuclear Technologies, Technical University of Denmark, DTU Risø Campus, DK-4000 Roskilde, Denmark
  • 4Nordic Laboratory for Luminescence Dating, Department of Geoscience, Aarhus University, DTU Risø Campus, DK-4000 Roskilde, Denmark
  • 5Geography & Earth Sciences, Aberystwyth University, Aberystwyth, Wales, United Kingdom
  • 6Jean Leray Laboratory of Mathematics (LMJL), UMR6629 CNRS - Université de Nantes, France

Abstract. Statistical analysis has become increasingly important in the field of OSL dating since it has become possible to measure signals at the single grain scale. The accuracy of large chronological datasets can benefit from the inclusion, in chronological modelling, of stratigraphic constraints and shared systematic errors. Recently, a number of Bayesian models have been developed for OSL age calculation; the R package BayLum allows implementing different such models, in particular for samples in stratigraphic order which share systematic errors. We first show how to introduce stratigraphic constraints in BayLum; then, we focus on the construction, based on measurement uncertainties, of dose covariance matrices to account for systematic errors specific to OSL dating. The nature (systematic versus random) of errors affecting OSL ages is discussed, based – as an example – on the dose rate determination procedure at the IRAMAT-CRP2A laboratory (Bordeaux). The effects of the stratigraphic constraints and dose covariance matrices are illustrated on example datasets. In particular, the interest of combining the modelling of systematic errors with independent ages, unaffected by these errors, is demonstrated. Finally, we discuss other common ways of estimating dose rates and how they may be taken into account in the covariance matrix by other potential users and laboratories. Test datasets are provided as supplementary material to the reader, together with an R Markdown tutorial allowing to reproduce all calculations and figures presented in this study.

Guillaume Guérin et al.

 
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Status: closed
Status: closed
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Guillaume Guérin et al.

Guillaume Guérin et al.

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Short summary
This paper demonstrates how to model OSL and radiocarbon ages in a Bayesian framework, using a dedicated software tool called BayLum. We show the effect of stratigraphic constraints, of modelling the covariance of ages when the same equipment is used for a series of OSL samples, and of including independent ages on a chronological inference. The improvement in chronological resolution is significant.