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\begin{document}

\title{Bias Corrected Estimation of Paleointensity (BiCEP): An improved
methodology for obtaining paleointensity estimates}



\author[1,1]{Brendan J Cych}%
\author[2,2]{Matthias Morzfeld}%
\author[3,3]{Lisa Tauxe}%
\affil[1]{Scripps Institution of Oceanography}%
\affil[2]{UCSD}%
\affil[3]{University of California, San Diego}%


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  \date{May 29, 2024}


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\begin{abstract}
The assumptions of paleointensity experiments are violated in many
natural and archaeological materials, leading to Arai plots which do not
appear linear and yield inaccurate paleointensity estimates, leading to
bias in the result. Recently, paleomagnetists have adopted sets of
``selection criteria'' that exclude specimens with non linear Arai plots
from the analysis, but there is little consensus in the paleomagnetic
community on which set to use. In this paper, we present a statistical
method we call Bias Corrected Estimation of Paleointensity (BiCEP),
which assumes that the paleointensity recorded by each specimen is
biased away from a true answer by an amount that is dependent a single
metric of nonlinearity (the curvature parameter
\$\textbackslash{}vec\{k\}\$) on the Arai plot. We can use this
empirical relationship to estimate the recorded paleointensity for a
specimen where \$\textbackslash{}vec\{k\}=0\$, i.e., a perfectly
straight line. We apply the BiCEP method to a collection of 30 sites for
which the true value of the original field is well constrained. Our
method returns accurate estimates of paleointensity, with similar levels
of accuracy and precision to restrictive sets of paleointensity
criteria, but accepting as many sites as permissive criteria. The BiCEP
method has a significant advantage over using these selection criteria
because it achieves these accurate results without excluding large
numbers of specimens from the analysis. It yields accurate, albeit
imprecise estimates from sites whose specimens all fail traditional
criteria. BiCEP combines the accuracy of the strictest selection
criteria with the low failure rates of the less reliable `loose'
criteria.%
\end{abstract}%



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