Sample characterization using both neutron and gamma multiplicities

Formulas for multiplicity counting rates (singles, doubles, etc.), used for the unfolding of parameters of an unknown sample, can be derived from those for the corresponding factorial moments. So far such rates were derived only for neutrons. The novelty of this paper is related to the derivation of the individual gamma and mixed neutron-gamma detection rates as well as to investigation of the possibilities and actual algorithms for the sample parameter unfolding. Taking the individual gamma and mixed neutron-gamma moments up to third order, together with the neutron moments, there will be nine auto- and cross-factorial moments and corresponding multiplicity rates, as well as a larger number of unknowns than for pure neutron detection. The total number of measurable multiplicities exceeds the number of unknowns, but on the other hand the structure of the additional equations is substantially more complicated than that of the neutron moments. Since an analytical inversion of the highly non-linear system of over-determined equations is not possible, the use of artificial neural networks (ANNs) is suggested, which can handle both the non-linearity and the redundance in the measured quantities in an effective and accurate way. The use of ANNs is demonstrated with good results on the unfolding of various combination of multiplicities for certain combinations of the unknown parameters, including the sample fission rate, the leakage multiplication and the alpha and gamma ratios. (C) 2010 Elsevier B.V. All rights reserved