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. Statistical analysis has become increasingly important in opticallystimulated luminescence (OSL) dating since it has become possible to measuresignals at the single-grain scale. The accuracy of large chronologicaldatasets can benefit from the inclusion, in chronological modelling, ofstratigraphic constraints and shared systematic errors. Recently, a numberof Bayesian models have been developed for OSL age calculation; the Rpackage “BayLum” presented herein allows different models of this type to be implemented,particularly for samples in stratigraphic order which share systematicerrors. We first show how to introduce stratigraphic constraints inBayLum; then, we focus on the construction, based on measurementuncertainties, of dose covariance matrices to account for systematic errorsspecific to OSL dating. The nature (systematic versus random) of errorsaffecting OSL ages is discussed, based – as an example – on the dose ratedetermination procedure at the IRAMAT-CRP2A laboratory (Bordeaux). Theeffects of the stratigraphic constraints and dose covariance matrices areillustrated on example datasets. In particular, the benefit of combiningthe modelling of systematic errors with independent ages, unaffected bythese errors, is demonstrated. Finally, we discuss other common ways ofestimating dose rates and how they may be taken into account in thecovariance matrix by other potential users and laboratories. Test datasetsare provided as a Supplement to the reader, together with an Rmarkdown tutorial allowing the reproduction of all calculations and figurespresented in this study.

geochronology

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