Program - Brookhaven National Laboratory

physicist. - There is no perfect set of model parameters but there are sets better than the others, the best
selected by their score values. - The quantification of the uncertainty can be approached in a global way
by applying a shape of the statistical error around a reference cross section calculated using the selected
sets.
PR 128
Covariance Matrices of Uncertainties in the ABBN Data System
Olga Andrianova, Yury Golovko, Gennady Jerdev, Dmitry Zadornov, Vladimir Koscheev, Gennady
Manturov, Anatoly Tsibulya, Institute for Physics and Power Engineering, Bondarenko Square 1,
Obninsk 244033, Kaluga Region, Russia.
The paper presents description of covariance matrices of uncertainties in the current version of the ABBN
group data system, which was obtained from the RUSFOND2010 nuclear data files. The ABBN covariance
data are compared to similar data from other sources. The uncertainties caused by nuclear data for the
most important neutron-physical parameters of several test models of perspective liquid metal-cooled fast
reactors were calculated using the ABBN covariance data. The analysis of the major contributions to the
estimated uncertainties was fulfilled and results are discussed.
Corresponding author: Genndy Manturov
1.A.A. Blyskavka, G.N. Manturov, M.N. Nikolaev, A.M. Tsiboulia. “Multigroup Monte Carlo Code MMK-
KENO,” Preprint IPPE-3145, Institute for Physics and Power Engineering, Obninsk (2009). (In Russian.)
2.“Multigroup Constant Set for Calculation of Neutron and Photon Radiation Fields and Functionals, Including
the CONSYST2 Program,” ORNL. RSICC DLC-182 (1995). 3.http://www-nds.iaea.org/exfor/endf.htm
PR 129
Adequate Treatment of Correlated Experimental Data in Nuclear Data Evaluations
Avoiding Pelle’s Pertinent Puzzle
D. Neudecker, Los Alamos National Laboratory, T-2, Theoretical Division Nuclear and Particle Physics,
Astrophysics & Cosmology. R. Fruehwirth, Institute of High Energy Physics of the Austrian Academy of
Sciences, Nikolsdorfer Gasse 18, A-1050 Vienna, Austria. H. Leeb, Institute of Atomic and Subatomic
Physics, Wiedner Hauptstrasse 8-10, A-1040 Vienna, Austria.
Peelle’s Pertinent Puzzle is a long-standing problem of nuclear data evaluation: Namely, one obtains unexpected
mean values and variances when combining experimental data affected by statistical and systematic
uncertainties by weighing with an experimental covariance matrix. This occurs for non-linear functions of
statistical quantities, e.g. for a product. In [1], it was shown in terms of Bayesian Statistics that Peelle’s
Pertinent Puzzle is primarily caused by an improper estimate of the experimental covariance matrix.
However, this was only demonstrated by means of two experimental data points for the same theoretical
quantity. In this contribution, the solution is tested for more than one theoretical quantity and is applied
to a typical nuclear data problem.
[1] ”Peelle’s Pertinent Puzzle and its Solution,” R. Fruehwirth, D. Neudecker, H. Leeb, EPJ Web of Conferences
00008, 27 (2012)
PR 130
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