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Handling Overdispersion in CRONUS-Earth Intercomparison Measurements: A Bayesian Approach

Published online by Cambridge University Press:  08 May 2017

Paul Muzikar ,
Brent Goehring  and
Nathaniel Lifton
Paul Muzikar*
Affiliation:
Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907
Brent Goehring
Affiliation:
Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118
Nathaniel Lifton
Affiliation:
Department of Earth, Atmospheric, and Planetary Sciences and Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907
*
*Corresponding author. Email: [email protected].
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Abstract

The recently completed CRONUS-Earth project broadly studied the production systematics of terrestrial in-situ cosmogenic nuclides and also incorporated an intercomparison study in which multiple labs measured various nuclides in homogenized geologic materials. Aliquots of these materials were measured in several labs for multiple nuclides, and the results combined to determine benchmark, consensus values. Results for some of these samples exhibited overdispersion, meaning that the measurements from the various labs differed by more than we would expect, given the quoted uncertainties. A traditional way to handle overdispersion is to add an extra amount to the variance of each lab. Another approach is to use a method that identifies potential outliers and then gives the outliers less weight in determining the final answer. A group of such methods is based on Bayesian thinking; one relatively simple member of this group was first proposed by Press (1997). We review the Press method and then apply it to the CRONUS data sets. We compare these results to those obtained by the added variance method and discuss the implications.

Keywords

Bayesian analysiscosmogenic nuclides
Type
Research Article
Information
Radiocarbon , Volume 59 , Issue 4 , August 2017 , pp. 1133 - 1145
Copyright
© 2017 by the Arizona Board of Regents on behalf of the University of Arizona 

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References

Blaauw, M, Bakker, R, Christen, JA, Hall, VA, van der Plicht, J. 2007. A Bayesian framework for age modeling of radiocarbon-dated peat deposits: case studies from the Netherlands. Radiocarbon 49(2):357–367.Google Scholar
Blard, P-H, Balco, G, Burnard, PG, Farley, KA, Fenton, CR, Friedrich, R, Jull, AJT, Niedermann, S, Pik, R, Schaefer, JM, Scott, EM, Shuster, DL, Stuart, FM, Stute, M, Tibari, B, Winckler, G, Zimmermann, L. 2015. An inter-laboratory comparison of cosmogenic 3He and radiogenic 4He in the CRONUS-P pyroxene standard. Quaternary Geochronology 26:11–19.Google Scholar
D’Agostini, G. 2003. Bayesian Reasoning in Data Analysis. World Scientific.Google Scholar
Bevington, PR, Robinson, DK. 2003. Data Reduction and Error Analysis. McGraw-Hill.Google Scholar
Bronk Ramsey, C. 2009. Dealing with outliers and offsets in radiocarbon dating. Radiocarbon 51(3):1023–1045.Google Scholar
Christen, JA. 1994. Summarizing a set of radiocarbon determinations: a robust approach. Applied Statistics 43:489–503.Google Scholar
Christen, JA, Perez, ES. 2009. A new robust statistical model for radiocarbon data. Radiocarbon 51(3):1047–1059.Google Scholar
Dose, V. 2003. Bayesian inference in physics: case studies. Reports on Progress in Physics 66:1421–61.CrossRefGoogle Scholar
Galbraith, RF, Laslett, GM. 1993. Statistical models for mixed fission track ages. Nuclear Tracks and Radiation Measurements 21:459–470.Google Scholar
Hanson, KM. 2005. Bayesian analysis of inconsistent measurements of neutron cross sections. In: Knuth K, Abbas AE, Morris RD, Castle JP, editors. AIP Conference Proceedings 803:431–9.Google Scholar
Jull, AJT, Scott, EM, Bierman, P. 2015. The CRONUS-Earth inter-comparison for cosmogenic isotope analysis. Quaternary Geochronology 26:3–10.Google Scholar
Lifton, N, Goehring, B, Wilson, J, Kubley, T, Caffee, M. 2015. Progress in automated extraction and purification of in-situ 14C from quartz: results from the Purdue in-situ 14C laboratory. Nuclear Instruments and Methods in Physics Research B 361:381–386.CrossRefGoogle Scholar
Phillips, FM, Argento, DC, Balco, G, Caffee, MW, Clem, J, Dunai, TJ, Finkel, R, Goehring, B, Gosse, JC, Hudson, AN, Jull, AJT, Kelly, MA, Kurz, M, Lal, D, Lifton, N, Marrero, SM, Nishiizumi, K, Reedy, RC, Schaefer, J, Stone, JOH, Swanson, T, Zreda, MG. 2016. The CRONUS-Earth Project: a synthesis. Quaternary Geochronology 31:119–154.CrossRefGoogle Scholar
Press, WH. 1997. Understanding data better with Bayesian and global statistical methods. In: Bahcall JN, Ostriker JP, editors. Unsolved Problems in Astrophysics. Princeton University Press. p 49–60.Google Scholar
Vermeesch, P. 2009. RadialPlotter: a Java application for fission track, luminescence and other radial plots. Radiation Measurements 44:409–410.CrossRefGoogle Scholar
Vermeesch, P, Balco, G, Blard, P-H, Dunai, TJ, Kober, F, Niedermann, S, Shuster, DL, Strasky, S, Stuart, FM, Wieler, R, Zimmermann, L. 2015. Interlaboratory comparison of cosmogenic 21Ne in quartz. Quaternary Geochronology 26:20–28.Google Scholar