The Korea Institute of Nuclear Nonproliferation and Control (KINAC) is developing a simulation model to estimate nuclear material production. This model is a foundational technology in interpretation and evaluation in preparation for denuclearization verification. Through this model, it is possible to estimate the amount of nuclear material that can be produced based on information on the activities of facilities related to the nuclear fuel cycle in the actual denuclearization verification stage. This model makes it possible to determine whether the declared amount of nuclear material is reliable. In addition, the reliability of the reported information can be confirmed through on-site inspection. However, there is a possibility that proliferation-related activities cannot be detected even through this inspection, and a normal state may be misdiagnosed as carrying out nuclear proliferation-related activities. Therefore, it is unreasonable to specify activities related to nuclear proliferation with only one inspection. Since each inspection method has its diagnosis rate and false diagnosis rate, measures such as repeating the same inspection method or combining different inspection methods are required to detect activities related to nuclear proliferation reliably. Therefore, a model capable of estimating the number of repetitions to obtain a reliable nuclear activity detection probability was developed by using each inspection method’s diagnosis rate and false diagnosis rate as input information through a Bayesian inference method. Through this model, it can be concluded that repetitive inspections increase the probability of detecting nuclear proliferation-related activities. This approach confirmed the possibility of repeatedly breaking away from the high-intensity inspection method that causes political and diplomatic resistance from the target country and substituting it with a more readily acceptable, low-intensity inspection method.

Concerns about North Korea’s 7th nuclear test have been rising recently, and it is a significant threat to the situation around the Korean Peninsula. Amidst these threats, the Korean government also shows a strong will for denuclearizing the Korean Peninsula, referring to the “Audacious Initiative.” For denuclearization negotiations with North Korea, it is essential first to understand North Korea’s nuclear capabilities. However, since access to information is complicated and contains many uncertainties, many studies have been conducted to estimate it. Among them, Von Hippel surveyed to estimate the total amount of uranium ore based on information on uranium mining, which is relatively widely known throughout North Korea’s nuclear fuel cycle, and the amounts of HEU and Pu suggested by many experts. KINAC has conducted a study on a methodology that can narrow the estimation range and improve reliability through the Bayesian Network based on Von Hippel’s research results. However, in this study, the probability distribution is assumed to be the simplest form of uniform distribution, and the estimation formula for the amount of Pu produced compared to the amount of uranium loaded in the core is used as it is, which is an error in Von Hippel’s study. Improvement is needed. This study proposes a more reliable BN model by supplementing this and attempts to estimate the amount of uranium ore that North Korea produces or possesses. Of course, the data used as the basic structure of the model is insufficient, and the estimation formula is straightforward, so it is somewhat unreliable to trust the estimate for uranium ore. However, it is expected to be a suitable methodology that can narrow the scope of North Korea’s nuclear material production estimate or compensate for the uncertainty of the nuclear material production estimation model being developed at KINAC.

Bayesian statistics, which is an approach to analyzing data based on Bayes’ theorem, is currently widely used in all fields. However, it has been applied very limitedly to studies related to nuclear nonproliferation. Therefore, this paper provides a knowledge base and directions for using various Bayesian techniques in nuclear non-proliferation. First, the concepts and advantages of the Bayesian approach are summarized and the basic solving methods of Bayesian inference are explained. The Bayesian approach enables more precise posterior estimation using the prior probability and the likelihood functions. To solve Bayes’ theorem, it is necessary to use the conjugate prior distribution, which is analytically solvable, or to use a numerical approach with computing power. Next, for several Bayesian statistics methods, the purpose of use and the mathematical derivation process are described. Bayesian linear regression analysis aims for obtaining a function that outputs the closest value to data of variables and results. Factor analysis is mainly used to derive a smaller number of unobserved latent variables that can represent observed variables. The logit and probit model are nonlinear regression models for when the outcome is binary. The hierarchical model is to analyze by introducing hyper-parameters in an integrated manner when there are several groups of similar data. The Bayesian approach of these methods is generally based on the numerical solution of the Bayesian inference of the multivariate normal distribution. Finally, the previous researches that each introduced method have been applied to nuclear non-proliferation are investigated, and research topics that can be applied in the future are suggested. Bayesian statistics have been mainly used for precise estimation of the amount, location, and radioactivity spectrum of nuclear materials using detectors. Using Bayesian approach, it will be possible to perform various analyzes. For example, the change of activeness of nuclear program can be estimated by Bayesian inferences on the frequency and scale of nuclear tests. And it can be tried predicting the production of plutonium according to the core configuration and burnup using the Bayesian linear regression. Also, by introducing the Bayesian approach to factor analysis or logit analysis of nuclear development motives or nuclear proliferation probability, it can be expected to improve precision. With the development of computer technology, the use of Bayesian statistics increases rapidly. Based on the theory and applied topics summarized in this paper, it is expected that Bayesian statistics will be more actively used for nuclear non-proliferation in the future.

Nuclear security event involving nuclear and other radioactive materials outside of regulatory control (MORC) has the potential to cause severe consequences for public health, the environment, the economy and society. Each state has a responsibility to develop national nuclear security measures including nuclear forensics to respond to such events. In Japan, national nuclear forensics capability building efforts mainly based on research and development (R&D) have been conducted since 2010, in accordance with national statement of Japan at the Nuclear Security Summit in Washington DC. Most of that work is undertaken at the Integrated Support Center for Nuclear Non-proliferation and Nuclear Security (ISCN) of the Japan Atmic Energy Agency (JAEA) in close cooperation with other competent authorities. The ISCN has made increased contributions to the enhancement of international nuclear security by establishing technical capabilities in nuclear forensics and sharing the achievements with the international community. The ISCN has mainly engaged in R&Ds for establishing and enhancing nuclear forensics technical capability. As for the laboratory capability, several new pieces of analytical equipment have been introduced for nuclear forensics R&D purposes. High-precise measurement techniques validated in the past nuclear forensics incidents have been established, and novel techniques that can contribute to the more timely and confident nuclear forensics signature analysis have been developed. The ISCN has been also developed a proto-type nuclear forensics library based on the data of nuclear materials possessed for past nuclear fuel cycle research in JAEA. These technical capability developments have been conducted based on the cooperation with international partners such as the U.S. Department of Energy and EC Joint Research Center, as well as participation in exercises organized by Nuclear Forensics International Technical Working Group (NF-ITWG). Recent R&D works have been mainly based on the needs of domestic competent authorities, such as first responders and investigators, and aim to develop technologies covering the entire spectrum of nuclear forensics processes from crime scene investigation to laboratory analysis and interpretation. One important key issue is the enhancement of technical capability for post-dispersion nuclear forensics. For instance, the ISCN has carried out the development of radiation measurement equipment coupled with the low-cost and mobile radiation detectors that uses machine-learning algorithms for quick and autonomous radioisotope identification to support first responders during crime scene investigations. Laboratory measurement techniques for samples collected at a post-dispersion crime scene are also among the important technical issues studied at the ISCN. The application of emerging technologies to nuclear forensics has also been studied. This includes the application of deep leaning models to nuclear forensics signature interpretation that could provide more confident results, as well as the development of contamination imaging technology that could contribute to the analytical planning on the samples in collaboration with conventional forensics. Many analytical techniques have been developed and the capability to analyze nuclear and other radioactive materials for nuclear forensics purposes has been considerably matured over the past decade. The challenges of post-dispersion samples, collaboration with conventional forensics and the development of novel signatures will be more important in the near future. Therefore, the ISCN will promote the R&Ds to further enhance the technical capabilities solving these issues. In addition, the ISCN is also promoting to expand the nuclear forensics research into universities and other research institutes in Japan. This is expected to contribute to the establishment of a domestic nuclear forensics network that enables to respond timely and flexibly to the MORC incidents, and to the maturation of nuclear forensics as a new academic field.

KINAC is trying to build a comprehensive aerial view of the nuclear material balance to predict North Korea’s weapons-grade nuclear material production capacity. We are creating a visualization model for North Korea’s nuclear facilities as part of these efforts. However, information on North Korea’s nuclear facilities is scarce, and it is not easy to consider additional facilities other than those already known. In addition, in the case of a model that targets only exceptional situations, it is not easy to secure objectivity for model validation, so it is necessary to upgrade to a general-purpose analysis tool that can be applied more generally. The following two examples are proposed as an analysis tool that can be a high degree of analysis. The first case is an Acquisition Path Analysis (APA) utilized to introduce IAEA’s State-Level Approach (SLA). The acquisition path analysis aims to find and evaluate the technically possible pathways to obtain nuclear materials for nuclear weapons or other nuclear explosive development. It can be an acquisition route if it is possible to produce at least 1 Significant Quantity (SQ) of weapongrade nuclear material within five years. The assessment of technologically feasible pathways is based on available information about the country’s past and present nuclear cycle capabilities. The second is the IAEA Physical Model. The IAEA Physical Model was carried out to introduce a comprehensive approach to all information on a country’s nuclear activities. It describes and characterizes the technologies and processes expressed at all levels of the acquisition path, depending on the development objectives. The IAEA Physical Model attempts a multi-tiered acquisition path analysis to identify all known technologies and processes in the nuclear fuel cycle, from raw material production to weapon usable material acquisition. Based on this analysis, the IAEA evaluates the signs of nuclear proliferation in a specific country. Based on the two cases discussed above, we intend to derive the following implications and priorities for extending the existing nuclear cycle model to a more general-purpose for a specific country. First of all, the requirements necessary to evaluate nuclear non-proliferation or verification of denuclearization must be at a level that the international community can recognize. In the stage of actual denuclearization verification, since verification will be conducted through the IAEA, a corresponding level of tools and technology will be required. From this point of view, the following is presented as a prerequisite for adding versatility to the existing physical model: It is necessary to derive all processes related to the nuclear cycle and standardize relevant indicators and data. In order to determine the signs of nuclear activity, detailed information on technologies, materials, by-products, and wastes, which are essential for each process, is required. For denuclearization verification, cumulative information from the past to the time point is required, and a comparative analysis of the operation history information of all facilities and the amount of nuclear material is required. To this end, it is necessary to make it possible to trace the history at every point where it can be determined that nuclear material has been diverted so that missing nuclear material can be found. Based on this, it is expected that it can be possible to evaluate a hypothetical threat state, but it is also expected that it will be easy to verify the model through the evaluation of easily accessible domestic facilities.