Abstract
We present new results on the identification and unfolding of neutron spectra from the pulse height distribution measured with liquid scintillators. The novelty of the method consists of the dual use of linear and nonlinear artificial neural networks (ANNs). The linear networks solve the superposition problem in the general unfolding problem, whereas the nonlinear networks provide greater accuracy in the neutron source identification problem. Two additional new aspects of the present approach are (i) the use of a very accurate Monte Carlo code for the simulations needed in the training phase of the ANNs and (ii) the ability of the network to respond to short-time and therefore very noisy experimental measurements. This approach ensures sufficient accuracy, timeliness, and robustness to make it a candidate of choice for the heretofore unaddressed nuclear nonproliferation and safeguards applications in which both identification and unfolding are needed.
Original language | English |
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Pages (from-to) | 742-752 |
Number of pages | 11 |
Journal | Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment |
Volume | 565 |
Issue number | 2 |
DOIs | |
State | Published - Sep 15 2006 |
Funding
The Oak Ridge National Laboratory is managed and operated for the US Department of Energy by UT-Battelle, LLC, under contract DE-AC05-00OR22725. This work was supported in part by the US Department of Energy National Nuclear Security Administration Office of Nonproliferation Research Engineering NA-22. We thank Klaus Ziock for his valuable comments on this manuscript.
Keywords
- Identification and unfolding
- Neural networks
- Neutron spectra
- Nuclear nonproliferation and safeguards
- Scintillation detector