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Ongoing nuclear modernization programs in Russia, China, and the USA have reopened longstanding debates among scholars concerning whether tailored nuclear weapons are likely to have destabilizing consequences for international security. Without data to adjudicate this debate, however, these discussions have remained entirely theoretical. In this article, we introduce an experimental wargaming platform, SIGNAL, to quantify the effect of tailored nuclear capabilities on the nuclear threshold in a simulated environment. We then compare these results with a survey experiment using scenarios related to military basing, cyber operations, and nuclear threats from the wargame environment. While the survey experiments suggest that the presence of tailored nuclear capabilities increases the likelihood of conflict escalation, this trend diminishes in the wargaming context. Across both data-generating processes, we find support for the proposition that lower-yield nuclear weapons are used as a substitute for their higher-yield counterparts. These results have consequences for recent and ongoing policy debates concerning strategic posture and the future of arms control. This work also makes methodological contributions to the design and application of experimental wargaming for social science research, particularly for scenarios where data are limited or non-existent.

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Recoil nuclei produce high ionization and excitation densities in organic scintillators leading to reduced light yield via ionization quenching. To improve understanding of the relationship between organic scintillator specific luminescence and the characteristics of the recoil particle, this work evaluates proton and carbon light yield data using ionization quenching models over an energy range of tens of keV to several MeV for protons and 1-5 MeV for carbon ions. Previously-measured proton and carbon light yield data were examined for a variety of commercial and novel organic scintillating media: EJ-309, a liquid with pulse shape discrimination (PSD) properties; EJ-204, a fast plastic; EJ-276, a PSD-capable plastic; and a custom organic glass scintillator developed by Sandia National Laboratories. The canonical model of Birks did not adequately describe the ionization quenching behavior. Models proposed by Yoshida et al. and Voltz et al. provided a reasonable description of the proton light yield of a variety of organic scintillators over a broad energy range, but additional work is needed to extend the models to carbon ions. The impact of stopping power data was also investigated by comparing model predictions using SRIM and PSTAR/MSTAR libraries, and the results show a significant discrepancy for carbon ions. This work enhances understanding of ionization quenching and facilitates the accurate modeling of scintillator-based neutron detection systems relevant for medical physics, nuclear security and nonproliferation, and basic science studies.

Accurate nuclear data provide an essential foundation for advances in a wide range of fields, including nuclear energy, nuclear safety and security, safeguards, nuclear medicine, and planetary and space exploration. In these and other critical domains, outdated, imprecise, and incomplete nuclear data can hinder progress, limit precision, and compromise safety. Similar nuclear data needs are shared by many applications, thus prioritizing these needs is especially important and urgently needed. Many levels of analysis are required to prepare nuclear measurements for employment in end-user applications. Because research expertise is typically limited to one level, collaboration across organizations and international borders is essential. This perspective piece provides the latest advances in nuclear data for applications and describes an outlook for both near- and long-term progress in the field.

The fusion Z-pinch experiment (FuZE) is a sheared-flow stabilized Z-pinch designed to study the effects of flow stabilization on deuterium plasmas with densities and temperatures high enough to drive nuclear fusion reactions. Results from FuZE show high pinch currents and neutron emission durations thousands of times longer than instability growth times. While these results are consistent with thermonuclear neutron emission, energetically resolved neutron measurements are a stronger constraint on the origin of the fusion production. This stems from the strong anisotropy in energy created in beam-target fusion, compared to the relatively isotropic emission in thermonuclear fusion. In dense Z-pinch plasmas, a potential and undesirable cause of beam-target fusion reactions is the presence of fast-growing, "sausage"instabilities. This work introduces a new method for characterizing beam instabilities by recording individual neutron interactions in plastic scintillator detectors positioned at two different angles around the device chamber. Histograms of the pulse-integral spectra from the two locations are compared using detailed Monte Carlo simulations. These models infer the deuteron beam energy based on differences in the measured neutron spectra at the two angles, thereby discriminating beam-target from thermonuclear production. An analysis of neutron emission profiles from FuZE precludes the presence of deuteron beams with energies greater than 4.65 keV with a statistical uncertainty of 4.15 keV and a systematic uncertainty of 0.53 keV. This analysis demonstrates that axial, beam-target fusion reactions are not the dominant source of neutron emission from FuZE. These data are promising for scaling FuZE up to fusion reactor conditions.

Above-ground nuclear explosions that interact with the surface of the earth entrain materials from the surrounding environment, influencing the resulting physical and chemical evolution of the fireball, which can affect the final chemical phase and mobility of hazardous radionuclides that are dispersed in the environment as fallout particles. The interaction of iron with a nuclear explosion is of specific interest due to the potential for iron to act as a redox buffer and because of the likelihood of significant masses of metals to be present in urban environments. We investigated fallout from a historic surface interacting nuclear explosion conducted on a steel tower and report the discovery of widespread and diverse iron-rich micro-structures preserved within the samples, including crystalline dendrites and micron-scale iron-rich spheres with liquid immiscibility textures. We assert these micro-structures reflect local redox conditions and cooling rates and can inform interpretation of high temperature events, enabling new insights into fireball condensation physics and chemistry when metals from the local environment (i.e. structural steel) are vaporized or entrained. These observations also significantly expand the availability of silicate immiscibility datasets applicable to rapidly quenched systems such as meteorite impact melt glass.

Herein we report the progress towards an organic glass scintillator with fast and thermal neutron sensitivity providing “triple” pulse shape discrimination (PSD) through the inclusion of a boron-incorporated aromatic molecule. The commercially available molecule 2-(p-tolyl)-1,3,2-dioxaborinane (TDB) can be readily synthesized in one step using inexpensive materials and incorporated into the organic glass scintillator at 20% by weight or 0.25% 10B by mass. In addition, we demonstrate that TDB can be easily scaled up and formulated into organic glass scintillator samples to produce a thermal neutron capture signal with a light yield equivalent to 120.4 ± 3.7 keVee, which is the highest value reported in the literature to date.

The multi-institution Single-Volume Scatter Camera (SVSC) collaboration led by Sandia National Laboratories (SNL) is developing a compact, high-efficiency double-scatter neutron imaging system. Kinematic emission imaging of fission-energy neutrons can be used to detect, locate, and spatially characterize special nuclear material. Neutron-scatter cameras, analogous to Compton imagers for gamma ray detection, have a wide field of view, good event-by-event angular resolution, and spectral sensitivity. Existing systems, however, suffer from large size and/or poor efficiency. We are developing high-efficiency scatter cameras with small form factors by detecting both neutron scatters in a compact active volume. This effort requires development and characterization of individual system components, namely fast organic scintillators, photodetectors, electronics, and reconstruction algorithms. In this presentation, we will focus on characterization measurements of several SVSC candidate scintillators. The SVSC collaboration is investigating two system concepts: the monolithic design in which isotropically emitted photons are detected on the sides of the volume, and the optically segmented design in which scintillation light is channeled along scintillator bars to segmented photodetector readout. For each of these approaches, we will describe the construction and performance of prototype systems. We will conclude by summarizing lessons learned, comparing and contrasting the two system designs, and outlining plans for the next iteration of prototype design and construction.

National security decisions are driven by complex, interconnected contextual, individual, and strategic variables. Modeling and simulation tools are often used to identify relevant patterns, which can then be shaped through policy remedies. In the paper to follow, however, we argue that models of these scenarios may be prone to the complexity-scarcity gap, in which relevant scenarios are too complex to model from first principles and data from historical scenarios are too sparse - making it difficult to draw representative conclusions. The result are models that are either too simple or are unduly biased by the assumptions of the analyst. We outline a new method of quantitative inquiry - experimental wargaming - as a means to bridge the complexity-scarcity gap that offers human-generated, empirical data to inform a variety of model and simulation tasks (model building, calibration, testing, and validation). Below, we briefly describe SIGNAL - our first-of-a-kind experimental wargame designed to study strategic stability in conflict settings with nuclear weapons. We then highlight the potential utility of this data for modeling and simulation efforts in the future using this data.

For over 40 years, physicists have considered possible uses for neutrino detectors in nuclear nonproliferation, arms control, and fissile materials security. Neutrinos are an attractive fission signature because they readily pass through matter. The same property makes neutrinos challenging to detect in systems that would be practical for nuclear security applications. This Colloquium presents a broad overview of several potential neutrino applications, including the near-field monitoring of known reactors, far-field monitoring of known or discovery of undeclared reactors, detection of reactor waste streams, and detection of nuclear explosions. Recent detector advances have made near-field monitoring feasible, whereas farther-field reactor detection and waste stream detection monitoring may be possible in some cases with further research and development. Very long-range reactor monitoring and nuclear explosion detection do not appear feasible for the foreseeable future due to considerable physical and/or practical constraints.

States facing the decision to develop a nuclear weapons program do so within a broader context of their relationships with other countries. How these diplomatic, economic, and strategic relationships impact proliferation decisions, however, remains under-specified. Adding to the existing empirical literature that attempts to model state proliferation decisions, this article introduces the first quantitative heterogeneous network analysis of how networks of conflict, alliances, trade, and nuclear cooperation interact to spur or deter nuclear proliferation. Using a multiplex network model, we conceptualize states as nodes linked by different modes of interaction represented on individual network layers. Node strength is used to quantify factors correlated with nuclear proliferation and these are combined in a weighted sum across layers to provide a metric characterizing the proliferation behavior of the state. This multiplex network modeling approach provides a means for identifying states with the highest relative likelihood of proliferation—based only on their relationships to other states. This work demonstrates that latent conflict and nuclear cooperation are positively correlated with proliferation, while an increased trade dependence suggests a decreased proliferation likelihood. A case study on Iran’s controversial nuclear program and past nuclear activity is also provided. These findings have clear, policy-relevant conclusions related to alliance posture, sanctions policy, and nuclear assistance.

Technology enables new research designs, and more data

While the nonproliferation community has long acknowledged the possibility of nuclear terrorism, its prevention has become a central focus area in recent years. For decades it has been assumed that, with access to special nuclear material (SNM), the steps to indigenously develop an improvised nuclear device are within the reach of non-state actors. At the same time, indigenous production of SNM has generally been dismissed as infeasible. Recognizing how recent trends of technology democratization, the open exchange of information, and globalization have eroded certain barriers to proliferation by non-state actors, this research explores the pathways a non-state actor could take to indigenously develop SNM and develops a method to determine the comparative attractiveness of each pathway, given the current capabilities of non-state actors. Additionally, it considers avenues for further investigation to identify countermeasures for SNM production, including countermeasures specific to non-state actors.

With the continued use of unsafeguarded naval nuclear-propulsion programs in all nuclear-weapon states, the commissioning of an Indian nuclear submarine, and the potential investment in such programs by non-nuclear-weapon states including Brazil and South Korea, movement toward a regulatory regime for nuclear material in the naval sector has become imperative. Such a framework faces a recurring debate on adequately protecting sensitive military technology while delivering assurances that naval nuclear material is not diverted to nuclear-weapon programs. In this viewpoint, we examine various prospective mechanisms to regulate naval nuclear stocks and assess them in terms of their effectiveness and scope. Drawing on lessons from the drafting, negotiation, and implementation of the Model Additional Protocol, we recommend a safeguards regime for naval nuclear material via a protocol that supplements the existing global nuclear-governance system. This protocol provides a standardized yet flexible approach to naval nuclear-material safeguards across all states (whether nuclearweapon states, non-nuclear-weapon states, or outside the Treaty on the Non-Proliferation of Nuclear Weapons) to handle variations among naval nuclear fuel cycles and technologies.

Angular momentum changes due to nuclear-plasma interactions on highly-excited nuclei in high energy density plasmas created at the National Ignition Facility can be measured through a change in isomer feeding following gamma emission. We propose an experiment to detect these effects in 133Xe* in exploding pusher capsules.

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor's operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility's cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain. [ABSTRACT FROM AUTHOR]