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Title: NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

Abstract

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.

Authors:
; ; ; ; ;  [1];  [2]
  1. Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico)
  2. Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)
Publication Date:
OSTI Identifier:
22121592
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1544; Journal Issue: 1; Conference: 9. international symposium on radiation physics, Puebla (Mexico), 14-17 Apr 2013; Other Information: (c) 2013 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 97 MATHEMATICAL METHODS AND COMPUTING; ARTIFICIAL INTELLIGENCE; BONNER SPHERE SPECTROMETERS; CALIFORNIUM 252; N CODES; NEURAL NETWORKS; NEUTRON SOURCES; NEUTRON SPECTRA; NEUTRON SPECTROSCOPY; PROGRAMMING; SPECTRA UNFOLDING; TOPOLOGY

Citation Formats

Ortiz-Rodriguez, J. M., Reyes Alfaro, A., Reyes Haro, A., Solis Sanches, L. O., Miranda, R. Castaneda, Cervantes Viramontes, J. M., and Vega-Carrillo, H. R.. NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres. United States: N. p., 2013. Web. doi:10.1063/1.4813469.
Ortiz-Rodriguez, J. M., Reyes Alfaro, A., Reyes Haro, A., Solis Sanches, L. O., Miranda, R. Castaneda, Cervantes Viramontes, J. M., & Vega-Carrillo, H. R.. NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres. United States. doi:10.1063/1.4813469.
Ortiz-Rodriguez, J. M., Reyes Alfaro, A., Reyes Haro, A., Solis Sanches, L. O., Miranda, R. Castaneda, Cervantes Viramontes, J. M., and Vega-Carrillo, H. R.. 2013. "NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres". United States. doi:10.1063/1.4813469.
@article{osti_22121592,
title = {NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres},
author = {Ortiz-Rodriguez, J. M. and Reyes Alfaro, A. and Reyes Haro, A. and Solis Sanches, L. O. and Miranda, R. Castaneda and Cervantes Viramontes, J. M. and Vega-Carrillo, H. R.},
abstractNote = {In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.},
doi = {10.1063/1.4813469},
journal = {AIP Conference Proceedings},
number = 1,
volume = 1544,
place = {United States},
year = 2013,
month = 7
}
  • The use of detectors moderated by Bonner spheres of different diameters is a relatively easy and inexpensive method of measuring the energy spectrum of neutron emission sources. Because the number of counts for each sphere diameter can be obtained by integration of the spectrum multiplied by a two-dimensional kernel, the spectrum is obtained from measurement data by means of a deconvolution or unfolding algorithm. Algorithms capable of solving this ill-posed inverse problem are based on iteration, requiring an initial spectrum estimate, extensive computation, and considerable experience on the part of the user. This paper presents a noniterative algorithm based onmore » spectrum models with undetermined parameters. It computes the set of parameters that minimizes the error between the actual Bonner counts measured and those predicted by integration of the resulting spectrum. Examples based on ideal data show that to avoid large spectrum errors caused by small measurement errors, the number of parameters involved must be small, much less than the number of sphere diameters employed. This restriction limits resolution in the spectrum, but the limitation is believed to be inherent in the physical characteristics of the Bonner system, not a defect of the algorithm. The method appears effective, fast, and easy to use.« less
  • In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetrymore » with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural net approach it is possible to reduce the rate counts used to unfold the neutron spectrum. To evaluate these codes a computer tool called Neutron Spectrometry and dosimetry computer tool was designed. The results obtained with this package are showed. The codes here mentioned are freely available upon request to the authors.« less
  • The Ricochet experiment seeks to measure Coherent (neutral-current) Elastic Neutrino-Nucleus Scattering using dark-matter-style detectors with sub-keV thresholds placed near a neutrino source, such as the MIT (research) Reactor (MITR), which operates at 5.5 MW generating approximately 2.2e18 neutrinos/second at the core. Currently, Ricochet is characterizing the backgrounds at MITR, the main component of which comes in the form of neutrons emitted from the core simultaneous with the neutrino signal. To characterize this background, we wrapped a Bonner cylinder around a He-3 thermal neutron detector, whose data was then unfolded to produce a neutron energy spectrum across several orders of magnitude.more » We discuss the resulting spectrum and its implications for deploying Ricochet in the future at the MITR site as well as the feasibility of reducing this background level via the addition of polyethylene shielding around the detector setup.« less
  • A determination of fast ion population parameters such as intensity and kinetic temperature is important for fusion reactors. This becomes more challenging with finer time resolution of the measurements, since the limited data in each time slice cause increasing statistical variations in the data. This paper describes a framework using Bayesian-regularized neural networks (NNs) designed for such a task. The method is applied to the TOFOR 2.5 MeV fusion neutron spectrometer at JET. NN training data are generated by random sampling of variables in neutron spectroscopy models. Ranges and probability distributions of the parameters are chosen to match the experimentalmore » data. Results have shown good performance both on synthetic and experimental data. The latter was assessed by statistical considerations and by examining the robustness and time consistency of the results. The regularization of the training algorithm allowed for higher time resolutions than simple forward methods. The fast execution time makes this approach suitable for real-time analysis with a time resolution limit in the microsecond time scale.« less
  • Determining the energy-dependent dose equivalent for neutrons is a difficult problem. The slowing-down process that neutrons undergo in moderating detectors destroys their incident energy information, causing the detector response to be a complicated function of energy. The improvement of neutron dosimetry requires experimental determination of neutron energy spectra in irradiation environments. Bonner spheres, which consist of a thermal-neutron scintillator and several polyethylene moderating spheres, are commonly used as a field neutron Spectrometer. A computer code must be used in tandem with the Bonner spheres to produce some approximate neutron spectrum from the sphere data by a technique known as spectralmore » unfolding. The unfolding technique requires at least one of several available Bonner sphere response matrices. The choice of response matrix may strongly affect the end-product spectrum. This paper describes the comparison of the several response matrices currently available.« less