Luminescence age calculation through Bayesian convolution of equivalent dose and dose-rate distributions: the <i>D</i>_e_<i>D</i>_r model

Abstract

In nature, each mineral grain (quartz or feldspar) receives a dose rate (Dr) specific to its environment. The dose-rate distribution therefore reflects the micro-dosimetric context of grains of similar size. If all the grains were well bleached at deposition, this distribution is assumed to correspond, within uncertainties, with the distribution of equivalent doses (De). The combination of the De and Dr distributions in the De_Dr model proposed here would then allow calculation of the true depositional age. If grains whose De values are not representative of this age (hereafter called “outliers”) are present in the De distribution, this model allows them to be identified before the age is calculated, enabling their exclusion. As the De_Dr approach relies only on the Dr distribution to describe the De distribution, the model avoids any assumption about the shape of the De distribution, which can be difficult to justify. Herein, we outline the mathematical concepts of the De_Dr approach (more details are given in Galharret et al., 2021) and the exploitation of this Bayesian modelling based on an R code available in the R package “Luminescence”. We also present a series of tests using simulated Dr and De distributions with and without outliers and show that the De_Dr approach can be an alternative to available models for interpreting De distributions.