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Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation
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Author(s): Pola Lydia Lagari (Purdue University, USA), Vladimir Sobes (Oak Ridge National Laboratory, USA), Miltiadis Alamaniotis (University of Utah, USA & Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA)and Lefteri H. Tsoukalas (Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA)
Copyright: 2020
Pages: 12
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch046
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Abstract
Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.
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