Alexander Glaser

Neutron sources can play a variety of roles in warhead verification. For transmission radiography, a source of directed high energy neutrons is required, while for applications to detect fissile isotopes, sub-MeV neutrons are preferred. The Excalibur (Experiment for Calibration with Uranium) neutron source has been built and used in a variety of verification-related experiments. Excalibur is based on a commercial deuterium-tritium neutron generator specified and measured to be capable of producing 14 MeV neutrons at rates of up to 8.2 × 10 8 neutrons/s. The generator is enclosed in a carbon-steel 32 ′ ′ diameter, 23.62 ′ ′ high carbon-steel cylinder that moderates the mean neutron energy to under 500 keV. This, in turn, is encased in 5%-borated polyethylene such that the entire assembly is a 48 ′ ′ × 4 8 ′ ′ box that is 30 ′ ′ tall. For radiographic applications, a narrow, tapered channel in the steel and polyethylene allows 14 MeV neutrons to stream directly from the generator to a test object. Its collimating capability is demonstrated by measuring the neutron flux profile. In the moderated mode of operation, the generator is fully enclosed in the steel, but a large section of the polyethylene is removed, providing a flux of sub-MeV neutrons from a wide range of angles. Neutron angular and spectral measurements using both a nested neutron spectrometer and a commercial liquid scintillator coupled with a 3 He detector show the expected softer neutron spectrum in moderated mode in good agreement with MCNP6 calculations. The gamma-ray spectrum from Excalibur is also in good agreement with MCNP modeling. Based on these findings, the future application of Excalibur in its two configurations is discussed.

Our work explores North Korea's 100 MW-th Experimental Light Water Reactor (ELWR) and its potential contributions to the country's nuclear weapons program. Built at the Yongbyon Nuclear Research Center, the ELWR began operations in October 2023 and represents North Korea's first attempts at a light-water reactor using domestically-enriched, ceramic fuel. Our study examines possible configurations for energy, tritium, and tritium-plutonium co-production. Assuming a single-batch core, the ELWR can be used to annually produce 48-82 grams of tritium, which can supply 2-4 new boosted warheads each year, up to a maximum arsenal of 88-150 warheads total. Concurrent production of tritium and weapon-grade plutonium is also possible but requires reprocessing of spent ceramic fuel. These findings underscore how North Korea's nuclear capabilities may be advanced through the ELWR's dual-use potential.

Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially secret nature of observable features, we propose a robotic verification procedure that provides map-free exploration to perform a source verification task without requiring, nor revealing, any task-irrelevant, site-specific information. We provide theoretical guarantees on the privacy and correctness of our approach, validated by extensive simulated and hardware experiments.

Generative deep learning architectures can produce realistic, high-resolution fake imagery -- with potentially drastic societal implications. A key question in this context is: How easy is it to generate realistic imagery, in particular for niche domains. The iterative process required to achieve specific image content is difficult to automate and control. Especially for rare classes, it remains difficult to assess fidelity, meaning whether generative approaches produce realistic imagery and alignment, meaning how (well) the generation can be guided by human input. In this work, we present a large-scale empirical evaluation of generative architectures which we fine-tuned to generate synthetic satellite imagery. We focus on nuclear power plants as an example of a rare object category - as there are only around 400 facilities worldwide, this restriction is exemplary for many other scenarios in which training and test data is limited by the restricted number of occurrences of real-world examples. We generate synthetic imagery by conditioning on two kinds of modalities, textual input and image input obtained from a game engine that allows for detailed specification of the building layout. The generated images are assessed by commonly used metrics for automatic evaluation and then compared with human judgement from our conducted user studies to assess their trustworthiness. Our results demonstrate that even for rare objects, generation of authentic synthetic satellite imagery with textual or detailed building layouts is feasible. In line with previous work, we find that automated metrics are often not aligned with human perception -- in fact, we find strong negative correlations between commonly used image quality metrics and human ratings.

Our work explores North Korea’s 100 MW-th Experimental Light Water Reactor (ELWR) and its potential contributions to the country’s nuclear weapons program. Built at the Yongbyon Nuclear Research Center, the ELWR began operations in October 2023 and represents North Korea’s first attempts at a light-water reactor using domestically-enriched fuel. Our study examines possible configurations for energy generation, tritium production, and tritium-plutonium co-production. Assuming a single-batch core, the ELWR can be used to annually produce 48–82 grams of tritium, which can supply 2–4 new boosted warheads each year, up to a maximum arsenal of 88–150 warheads total. Concurrent production of tritium and weapon-grade plutonium is also possible but requires reprocessing of spent ceramic fuel. Overall, the findings underscore how North Korea’s nuclear capabilities may be advanced through the ELWR’s dual-use potential.

Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest. Relevant imagery may however not be available at sufficiently high resolution, quality, or cadence -- let alone be uniformly accessible to open-source analysts. This limits an assessment of the true long-term potential of citizen-based monitoring of nuclear activities using publicly available satellite imagery. In this article, we demonstrate how modern game engines combined with advanced machine-learning techniques can be used to generate synthetic imagery of sites of interest with the ability to choose relevant parameters upon request; these include time of day, cloud cover, season, or level of activity onsite. At the same time, resolution and off-nadir angle can be adjusted to simulate different characteristics of the satellite. While there are several possible use-cases for synthetic imagery, here we focus on its usefulness to support tabletop exercises in which simple monitoring scenarios can be examined to better understand verification capabilities enabled by new satellite constellations and very short revisit times.

Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification.

Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially secret nature of observable features, we propose a robotic verification procedure that provides map-free exploration to perform a source verification task without requiring, nor revealing, any task-irrelevant, site-specific information. We provide theoretical guarantees on the privacy and correctness of our approach, validated by extensive simulated and hardware experiments.

Remotely monitoring the location and enduring presence of valuable items in adversary-controlled environments presents significant challenges. In this article, we demonstrate a monitoring approach that leverages the gigahertz radio-wave scattering and absorption of a room and its contents, including a set of mirrors with random orientations placed inside, to remotely verify the absence of any disturbance over time. Our technique extends to large physical systems the application of physical unclonable functions for integrity protection. Its main applications are scenarios where parties are mutually distrustful and have privacy and security constraints. Examples range from the verification of nuclear arms-control treaties to the securing of currency, artwork, or data centers.

In March 2023, the UN Institute for Disarmament Research held a verification experiment that included a mockup onsite inspection at a former military facility in the municipality of Menzingen, Switzerland. The experiment included a visit to the site by an inspection team, accompanied by the host team. Among other activities, radiation measurements were used to confirm the non-nuclear nature of selected items stored onsite. In this paper, we discuss the neutron and gamma measurement systems used during the experiment and the inspection protocols followed to confirm the absence of nuclear weapons. Results from the experiment and a laboratory reproduction are presented, before concluding with lessons learned for how absence-confirmation measurements can help support verification of future arms control agreements.

The Menzingen Verification Experiment described in this report was designed to test practical procedures for verifying the absence of nuclear weapons at a storage site. The experiment, which was conducted on 8 March 2023, was organized by UNIDIR in partnership with the Swiss Armed Forces, Spiez Laboratory, Princeton University’s Program on Science and Global Security, and the Open Nuclear Network. The project was supported by the Governments of the Kingdom of the Netherlands, Norway, and Switzerland. The experiment modelled an on-site inspection of a nuclear weapons storage site, represented by a former air defence site near Menzingen, Switzerland. In preparation for the experiment, UNIDIR developed a model protocol governing the inspection activities. Together with its partners, it designed procedures to confirm the non-nuclear nature of the inspected items, including radiation measurements with active sources, and arranged for the acquisition of satellite imagery of the site. The scenario developed for the experiment assumed that the inspection was conducted as part of an agreement that requires the parties to remove all nuclear weapons from storage sites associated with military bases that host nuclear-capable delivery systems. The inspection procedures used in the experiment were modelled on those developed for the Conventional Forces in Europe Treaty and New START. The Menzingen Verification Experiment demonstrated in practice the viability of the approach to nuclear disarmament based on removing nuclear weapons from their delivery systems. It provided an opportunity to test in practice specific verification procedures and techniques, provided valuable insights into the challenges that can be encountered during an on-site inspection, and identified promising new approaches to verification that can create political space for arms control and disarmament initiatives.

The idea that citizens, including scientists, and civil society groups be responsible for the monitoring and verification of their state’s compliance with nuclear weapons reduction and prohibition treaties – complementary to state-level and international agency mechanisms – has had various iterations. Each iteration reflected a particular historical moment of nuclear politics and identities, state-society relations, and possibilities for communication and collective action. A 2023 Princeton University Program on Science and Global Security workshop explored contemporary civil-society practices related to nuclear program monitoring and verification given the prevalence of internet connectivity, online publicly available data and tools, and cheap ubiquitous sensing from mobile cameras to commercial satellites. The aim of the workshop was to assess the potential and limits of such societal verification efforts. The workshop had three broad themes: the present context of societies, technologies, markets, and states within which civil-society does nuclear-activity monitoring and verification; how the uneven, hierarchical national and international distribution of power and relevant resources is enabling, shaping, and limiting the potential contributions of civil society to national and global security debates and decision-making; and the factors underlying the trustworthiness of civil-society nuclear-activity monitoring and verification practices and their outcomes as well as the relationship between trust (in data, processes, and institutions) and balanced practices.