Classification of Spent Reactor Fuel for Nuclear Forensics

In this paper we demonstrate the use of pattern recognition and machine learning techniques to determine the reactor type from which spent reactor fuel has originated. This has been done using the isotopic and elemental measurements of the sample and proves to be very useful in the field of nuclear forensics. Nuclear materials contain many variables (impurities and isotopes) that are very difficult to consider individually. A method that considers all material parameters simultaneously is advantageous. Currently the field of nuclear forensics focuses on the analysis of key material properties to determine details about the materials processing history, for example, utilizing known half-lives of isotopes can determine when the material was last processed (Stanley, F. E. J. Anal. At. Spectrom. 2012, 27, 1821; Varga, Z.; Wallenius, M.; Mayer, K.; Keegan, E.; Millet, S. Anal. Chem. 2009, 81, 8327-8334). However, it has been demonstrated that multivariate statistical analysis of isotopic concentrations can complement these method and are able to make use of a greater level of information through dimensionality reduction techniques (Robel, M.; Kristo, M. J. J. Environ. Radioact. 2008, 99, 1789-1797; Robel, M.; Kristo, M. J.; Heller, M. A. Nuclear Forensic Inferences Using Iterative Multidimensional Statistics. In Proceedings of the Institute of Nuclear Materials Management 50th Annual Meeting, Tucson, AZ, July 2009; 12 pages; Nicolaou, G. J. Environ. Radioact. 2006, 86, 313-318; Pajo, L.; Mayer, K.; Koch, L. Fresenius' J. Anal. Chem. 2001, 371, 348-352). There has been some success in using such multidimensional statistical methods to determine details about the history of spent reactor fuel (Robel, M.; Kristo, M. J. J. Environ. Radioact. 2008, 99, 1789-1797). Here, we aim to expand on these findings by pursuing more robust dimensionality reduction techniques based on manifold embedding which are able to better capture the intrinsic data set information. Furthermore, we demonstrate the use of a number of classification algorithms to reliably determine the reactor type in which a spent fuel material has been irradiated. A number of these classification techniques are novel applications in nuclear forensics and expand on the existing knowledge in this field by creating a reliable and robust classification model. The results from this analysis show that our techniques have been very successful and further ascertain the excellent potential of these techniques in the field of nuclear forensics at least with regard to spent reactor fuel.