Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T03:37:12.576Z Has data issue: false hasContentIssue false
  • English
  • Français

Using car4ams, the Bayesian AMS Data Analysis Code

Published online by Cambridge University Press:  18 July 2016

V Palonen ,
P Tikkanen  and
J Keinonen
V Palonen*
Affiliation:
P.O. Box 43, Department of Physics, 00014 University of Helsinki, Finland
P Tikkanen
Affiliation:
P.O. Box 43, Department of Physics, 00014 University of Helsinki, Finland
J Keinonen
Affiliation:
P.O. Box 43, Department of Physics, 00014 University of Helsinki, Finland
*
Corresponding author. Email: vesa.palonen@helsinki.fi.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the GPL license. Included in the package is an R program that analyzes the car4ams output and summarizes the results in graphical and spreadsheet formats. We describe the main properties of the CAR analysis and the use of the 2 parts of the program package for radiocarbon AMS data analysis.

Type
Calibration, Data Analysis, and Statistical Methods
Information
Radiocarbon , Volume 52 , Issue 3: 20th Int. Radiocarbon Conference Proceedings , 2010 , pp. 948 - 952
Copyright
Copyright © 2010 by the Arizona Board of Regents on behalf of the University of Arizona 

References

Donahue, DJ, Linick, TW, Jull, AJT. 1990. Isotope-ratio and background corrections for accelerator mass spectrometry radiocarbon measurement. Radiocarbon 32(2):135–42.Google Scholar
Jaynes, ET. 2003. Probability Theory: The Logic of Science. Cambridge: Cambridge University Press.Google Scholar
Palonen, V. 2008. Accelerator mass spectrometry and bayesian data analysis [PhD dissertation]. University of Helsinki Report Series in Physics HU-P-D148. Helsinki: University of Helsinki, Department of Physics. URL: http://urn.fi/URN:ISBN:978-952-10-3262-2.Google Scholar
Palonen, V, Tikkanen, P. 2007. Pushing the limits of AMS radiocarbon dating with improved Bayesian data analysis. Radiocarbon 49(3)1261–72.Google Scholar
Palonen, V, Tikkanen, P, Keinonen, J. 2008a. A Bayesian measurement model; reliable uncertainties and control over instrumental drift. Journal of Physics D: Applied Physics 41:212001, doi:10.1088/0022-3727/41/21/212001.Google Scholar
Palonen, V, Tikkanen, P, Keinonen, J. 2008b. Improving AMS uncertainties and detection of instrumental error. Nuclear Instruments and Methods in Physics Research B 268(7–8):972–5.Google Scholar
Palonen, V, Tikkanen, P, Keinonen, J. 2009. car4ams: a program for Bayesian analysis of accelerator mass spectrometry data [program and manual]. Available at URL: http://beam.acclab.helsinki.fi/∼vpalonen/car4ams/.Google Scholar
R Development Core Team 2008. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0. URL: http://www.r-project.org.Google Scholar
Stuiver, M, Polach, HA. 1977. Discussion: reporting of 14C data. Radiocarbon 19(3):355–63.CrossRefGoogle Scholar
Tuniz, C, Bird, J, Fink, D, Herzog, GF. 1998. Accelerator Mass Spectrometry: Ultrasensitive Analysis for Global Science. Boca Raton: CRC Press. 400 p.Google Scholar