  In this manuscript we study the modeling of experimental data and its impact
on the resulting integral experimental covariance and correlation matrices. By
investigating a set of three low enriched and water moderated UO2 fuel rod
arrays we found that modeling the same set of data with different, yet
reasonable assumptions concerning the fuel rod composition and its geometric
properties leads to significantly different covariance matrices or correlation
coefficients. Following a Monte Carlo sampling approach, we show for nine
different modeling assumptions the corresponding correlation coefficients and
sensitivity profiles for each pair of the effective neutron multiplication
factor keff. Within the 95% confidence interval the correlation coefficients
vary from 0 to 1, depending on the modeling assumptions. Our findings show that
the choice of modeling can have a huge impact on integral experimental
covariance matrices. When the latter are used in a validation procedure to
derive a bias, this procedure can be affected by the choice of modeling
assumptions, too. The correct consideration of correlated data seems to be
inevitable if the experimental data in a validation procedure is limited or one
cannot rely on a sufficient number of uncorrelated data sets, e.g. from
different laboratories using different setups etc.
