This paper deals with the design of a novel spectrometer of fast neutrons in nuclear safeguards applications based on the liquid organic scintillator EJ-309 with materials of different thickness surrounding the detector. The investigation was performed on the simulated data obtained by the MCNPX-PoliMi numerical code based on the Monte Carlo method. Among the various materials (polyethylene, iron, aluminum, and graphite) investigated as layers around the scintillator, polyethylene and iron have shown the most promising characteristics for evaluation of fast neutron energy spectra. The simulated pulse height distributions were summed up for each energy bin in the neutron energy range between 1 MeV and 15 MeV in order to obtain better counting statistics. The unfolded results for monoenergetic neutron sources obtained by a first order of Tikhonov regularization and non-linear neural network show very good agreement with the reference data while the evaluated spectra of neutron sources continuous in energy follow the trend of the reference spectra. The possible advantages of a novel spectrometer include a less number of input data for processing and a less sensitivity to the noise compared to the scintillation detector without surrounding materials.

The knowledge of neutron energy spectra contributes to unambiguous identification of neutron sources in the fields of nuclear safeguards and nuclear non-proliferation. Since a real scenario situation includes the presence of shielding around the source, we have investigated the influence of the potential shielding surrounding the source on the shape of energy spectra for a few neutron sources. We have applied the maximum-likelihood, expectation–maximisation (MLEM) method with one-step-late (OSL) algorithm for neutron spectra unfolding. The pulse height distributions used in the unfolding procedures were simulated with the high accuracy by using the MCNP-PoliMi code based on the Monte Carlo method. A possibility to identify the shielded neutron sources by using the unfolding method was examined with two continuous-in-energy sources, such as 252Cf and 241Am–Be in source-shielding configurations with lead (Pb) and polyethylene (PE) blocks. The results of calculations have shown that the identification of 252Cf and 241Am–Be sources with 2.5 cm of Pb and PE shield can be achieved successfully by using the MLEM method with the OSL algorithm. However, the unfolded results for 252Cf and 241Am–Be sources with 10 cm of PE shield significantly deviate from the reference spectra and the sources cannot be correctly identified on the basis of their unfolded energy spectra.

This paper deals with the improvements of the linear artificial neural network unfolding approach aimed at accurately determining the incident neutron spectrum. The effects of the transfer functions and pre-processing of the simulated pulse height distributions from liquid scintillation detectors on the artificial neural networks performance have been studied. A better energy resolution and higher reliability of the linear artificial neural network technique have been achieved after implementation of the results of this study. The optimized structure of the network was used to unfold both monoenergetic and continuous neutron energy spectra, such as the spectra of 252Cf and 241Am-Be sources, traditionally used in the nuclear safeguards experiments. We have demonstrated that the artificial neural network energy resolution of 0.1 MeV is comparable with the one obtained by the reference maximum likelihood expectation-maximization method which was implemented by using the one step late algorithm. Although the maximum likelihood algorithm provides the unfolded results of higher accuracy, especially for continuous neutron sources, the artificial neural network approach with the improved performances is more suitable for fast and robust determination of the neutron spectra with sufficient accuracy.

This paper proposes a neutron source identification method based on the spectral analysis of neutron pulse height distributions obtained with liquid scintillation detectors. The fact that shielded and unshielded neutron sources have clearly defined spectral components with specific locations and intensities offers the possibility of identifying the sources based on spectral features alone, without having to unfold the energy spectra. Analysis of simulated and experimental data confirms that this new identification method is promising, and that good resolution power can be achieved.

Expressions for neutron and gamma factorial moments have been known in the literature. The neutron factorial moments have served as the basis of constructing analytic expressions for the detection rates of singles, doubles and triples, which can be used to unfold sample parameters from the measured neutron multiplicity rates. The gamma factorial moments can also be extended into detection rates of multiplets, as well as the combined use of joint neutron and gamma multiplicities and the corresponding detection rates. Counting up to third order, there are nine auto- and cross factorial moments. Adding the gamma counting to the neutrons introduces new unknowns, related to gamma generation, leakage, and detection. Despite of having more unknowns, the total number of independent measurable moments exceeds the number of unknowns. On the other hand, the structure of the additional equations is substantially more complicated than that of the neutron moments, hence the analytical inversion of the gamma moments alone is not possible. We suggest therefore to invert the non-linear system of over-determined equations by using artificial neural networks (ANN), which can handle both the non-linearity and the redundancies in the measured quantities in an effective and accurate way. The use of ANN is successfully demonstrated on the unfolding of neutron multiplicity rates for the sample fission rate, the leakage multiplication and the ratio. The analysis is further extended to unfold also the gamma related parameters. The stability and robustness of the ANNs is further investigated to verify the applicability of the method. The ANN approach enables extraction of additional important information on the fissile sample compared to the application of the analytical method.

Expressions for neutron and gamma factorial moments are known in the literature. For neutrons, these served as the basis of constructing analytic expressions for the detection rates of singles, doubles and triples, which can be used to unfold sample parameters from the measured multiplicity rates. Here we suggest the combined use of both the individual and joint neutron and gamma multiplicities and the corresponding detection rates. Counting up to third order, there are nine auto- and cross factorial moments, which are all given here explicitly. For the gamma photons, formulae are derived also for the corresponding multiplicity detection rates which, in contrast to the factorial moments, are the measured quantities and which also contain the sample fission rate explicitly. Adding the gamma counting to the neutrons introduces new unknowns, related to gamma generation, leakage, and detection. Despite more unknowns, the total number of measurable moments exceeds the number of unknowns. On the other hand, the structure of the additional equations is substantially more complicated than the neutron moments, hence their analytical inversion is not possible. We suggest therefore to invert the non-linear system of over-determined equations by using artificial neural networks (ANN), 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 neutron multiplicity rates for the sample fission rate, the leakage multiplication and the ? ratio. Work with using the gamma multiplicity rates is on-going and some results will be reported at the conference.

This paper is focused on the space and temporal variability of soil moisture experimental data acquired at a few locations near landmine fields in the Tuzla Canton, as well as on the quantification of the statistical nature of soil moisture data on a small spatial scale. Measurements of soil water content at the surface were performed by an electro-magnetic sensor over 1 25, and 100 m2 grids, at intervals of 0.2, 0.5, and 1 m, respectively. The sampling of soil moisture at different spatial resolutions and over different grid sizes has been investigated in order to achieve the quantification of the statistical nature of soil moisture distribution. The statistical characterization of spatial variability was performed through variogram and correlogram analysis of measurement results. The temporal variability of the said samples was examined over a two-season period. For both sampling periods, the spatial correlation length is about 1 to 2 m, respectively, or less. Thus, sampling should be done on a larger spatial scale, in order to capture the variability of the investigated areas. Since the characteristics of many landmine sensors depend on soil moisture, the results of this study could form a useful data base for multisensor landmine detection systems with a promising performance.

A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained from dynamic neutron radiography recordings. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical functions (mean and variance) of the wavelet transform coefficients of the pixel intensity data. The investigations show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves.

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pžzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.