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**Harvard**

Enqvist, A., Pázsit, I. och Avdic, S. (2010) *Sample characterization using both neutron and gamma multiplicities*.

** BibTeX **

@article{

Enqvist2010,

author={Enqvist, Andreas and Pázsit, Imre and Avdic, S.},

title={Sample characterization using both neutron and gamma multiplicities},

journal={Nuclear Instruments & Methods in Physics Research Section a-Accelerators Spectrometers Detectors and Associated Equipment},

issn={0168-9002},

volume={615},

issue={1},

pages={62-69},

abstract={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.},

year={2010},

keywords={Nuclear safeguards, Neutron and gamma multiplicities, Joint moments, Materials control and accounting, Neural networks, MULTIPLYING SAMPLE, FISSION, ASSAY },

}

** RefWorks **

RT Journal Article

SR Electronic

ID 121051

A1 Enqvist, Andreas

A1 Pázsit, Imre

A1 Avdic, S.

T1 Sample characterization using both neutron and gamma multiplicities

YR 2010

JF Nuclear Instruments & Methods in Physics Research Section a-Accelerators Spectrometers Detectors and Associated Equipment

SN 0168-9002

VO 615

IS 1

SP 62

OP 69

AB 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.

LA eng

DO 10.1016/j.nima.2010.01.022

LK http://dx.doi.org/10.1016/j.nima.2010.01.022

OL 30