Program - Brookhaven National Laboratory

A large part of Brazil and thus of South America is subject to the South Atlantic Magnetic Anomaly
(SAMA), which may modify the behavior of cosmic radiation showers as a function of altitude. Highenergy
neutrons are produced by primary cosmic ray interactions with atoms in the atmosphere through
spallation reactions and intranuclear cascade processes. These neutrons can produce secondary neutrons
and also undergo moderation due to atmospheric interactions, resulting in a wide energy spectrum, which
ranges from thermal energies (0.025 eV) to energies of several hundreds of MeV. The resulting cosmicray
induced neutron spectrum (CRINS) is essential for evaluating the dose accumulated in aircraft crew
members at flight altitude and the soft-error rates of semiconductor devices. It is thus very important
to understand the cosmic-ray induced neutron spectrum in the atmosphere. The goal of this study is to
assess the CRINS using the Monte Carlo computational codes MCNPX [1] and GEANT4 [2,3]. To do so,
it is necessary to reproduce the physical situation of a wide energy spectrum of protons incident from all
angles at the top of the atmosphere and propagating into it. The simulations were performed using physics
models at energies above those available in nuclear data libraries and tabulated evaluations of nuclear data
at lower energies.
Corresponding author: B. V. Carlson
[1] D. Pelowitz (Ed.), MCNPX User’s Manual Version 2.5.0, Los Alamos National Laboratory report LA-
CP-05-0369 (2005). [2] S. Agostinelli et al., Nuclear Instruments and Methods in Physics Research A 506
(2003) 250. [3] K. Amato et al., IEEE Transactions on Nuclear Science 53 (2006) 270.
PR 127
Statistical Building of a Hierarchy of Numerical Models with Deterministic Parameters for
Cross Section Uncertainty Evaluations
P. Dossantos-Uzarralde, S. Varet, S. Hilaire, CEA, DAM, DIF, F-91297 Arpajon, France. N. Vayatis,
ENS Cachan, France. F. Prido, Phelma-Grenoble, France.
Nuclear reaction models play a crucial role in today’s nuclear data evaluations. However, there are difficulties
associated with evaluating data uncertainties, both in performing the experimental measurements
as well as in building them from nuclear models. In this general context, our interest is targeted towards
the study of the propagation of uncertainties within nuclear models, and in particular the evaluation of nuclear
cross sections. Current approaches (Sensitivity propagation, Inverse Approach, Mean Squared Error,
Bayesian Estimation, ...) envisaged for the study of cross sections uncertainties introduce numerous modelization
biases whose impact on the announced uncertainty is unknown (parametric approaches, bayesian
presuppositions, artificial alea). Keeping the cross section uncertainties under control requires to take into
account two types of knowledge: a) the physical theory (and its implementation through the numerical
codes) and b) the experiment. In front of the variability observed in the experimental measures and the
sensibility of the numerical models with regards to their parameters, the statistical methods can supply
a support for the quantification of the uncertainties. In this paper, we propose a new global approach
intended to supply instruments for the comparison of the candidate cross sections. The basic hypothesis
consists in not modelling the physical and numerical code parameters as random quantities. The idea is to
sort the choices of modelling in a numerical code by level of adequacy (named score) with a set of preset
measures. We can also see this approach as an attempt to produce a score associated with every theoretical
model of cross section. We can also see our approach as a protocol to refute cross section evaluation or
experimental measure. Regression models used for a statistical approximation of the cross sections allow
in particular to take into account the constraint of regularity of the corresponding curves. Our approach
bases itself on the following presuppositions: - The selection of ”relevant” points of measures (in other
words, compatible with a theoretical model) can be automated while conforming to the criteria of the
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