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Pushing the Limits of AMS Radiocarbon Dating with Improved Bayesian Data Analysis

Published online by Cambridge University Press:  18 July 2016

V Palonen  and
P Tikkanen
V Palonen*
Affiliation:
Accelerator Laboratory, PO Box 43, FIN-00014 University of Helsinki, Finland
P Tikkanen
Affiliation:
Accelerator Laboratory, PO Box 43, FIN-00014 University of Helsinki, Finland
*
Corresponding author. Email: vesa.palonen@helsinki.fi
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Abstract

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We present an improved version of the continuous autoregressive (CAR) model, a Bayesian data analysis model for accelerator mass spectrometry (AMS). Measurement error is taken to be Poisson-distributed, improving the analysis for samples with only a few counts. This, in turn, enables pushing the limit of radiocarbon measurements to lower concentrations. On the computational side, machine drift is described with a vector of parameters, and hence the user can examine the probable shape of the trend. The model is compared to the conventional mean-based (MB) method, with simulated measurements representing a typical run of a modern AMS machine and a run with very old samples. In both comparisons, CAR has better precision, gives much more stable uncertainties, and is slightly more accurate. Finally, some results are given from Helsinki AMS measurements of background sample materials, with natural diamonds among them.

Type
Articles
Information
Radiocarbon , Volume 49 , Issue 3 , 2007 , pp. 1261 - 1272
Copyright
Copyright © 2007 by the Arizona Board of Regents on behalf of the University of Arizona 

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