(339e) Role of Nuclear Forensics Defined As a Digital Problem With Neurofuzzy Approach in Various Applications

Abstract

With this paper we introduce the importance of highly needed discipline in the country, the nuclear forensics. It applies to all fields of interest to nuclear science and engineering, chemical engineering and chemistry, and all other associated fields and disciplines. Nuclear forensics is a science highly relying on interdisciplinary knowledge culminating in precise analysis of samples of any origin in identifying their “true home”, in other words in finding where there were taken from, how they and if they were smuggled to a given location, and what were the pathways. Such analysis applies to many fields of interest to nuclear fuel cycle and chemistry, and consists of a (recently found to be of high value) digital data-based search process to find the origin of the sample based on its nuclear signature; such a signature includes measuring of specific samples properties using a predetermined method, for example gamma spectroscopy, and subsequent matching of such measurements to a set of known (digital) values. Recently, the importance of all steps in nuclear fuel cycle in the country in finding the best way for the future, elevated importance of an increasing volume of nuclear data as pertaining to nuclear fuel cycle/nuclear forensics being generated to be organized and accurately managed and analyzed. Therefore there is an emerging demand for developing new, fast and accurate ad-hoc methods in defining the nuclear signatures of real-time data analysis.

In this paper we present how the artificial intelligence technologies could be applied in nuclear fuel cycle data analysis in generating this novel and yet hard to collect and organize data pertaining to nuclear forensics based on developed and yet to be developed digital data libraries. In adopting a synergism of fuzzy logic and neural networks methods, we show how to analyze as an example a complex gamma spectroscopic data, and therefore attribute the nuclear fuel cycle needs. This neurofuzzy model is comprised of two modules: in (1) the model utilizes fuzzy logic to represent values of specific extracted material properties taking into account inherent uncertainties, and in (2) the fuzzified values are fed into a neural network which provides in its output a number to be used to designate origin of nuclear material. Before that, the neural network, which plays the role of digital library, is trained on a set of known data that uniquely characterize the sources of interest. This methodology allows for automated and fast analysis of incoming data, while eases the process of inference and decision-making. This method has been already tested on a set of various gamma spectra where the efficiency of the neurofuzzy model was well demonstrated.