Rethinking Porosity-Based Diffusivity Estimates for Sorptive Gas Transport at Variable Temperatures
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There is a growing realization that neutrinos can be used as a diagnostic tool to better understand the inner workings of a nuclear weapon. Robust estimates demonstrate that an Inverse Beta Decay (IBD) neutrino scintillation detector built at the Nevada Test Site with a 1000-ton active target mass at a standoff distance of 500 m would detect thousands of antineutrino events per nuclear test. This would provide less than 4% statistical error on the measured antineutrino rate and 5% error on antineutrino energy. Extrapolating this to an error on the test device explosive yield requires knowledge from evaluated nuclear databases, non-equilibrium fission rates, and assumptions on internal neutron fluxes. Initial calculations demonstrate that the total number of neutrinos emitted per fission in the first 10 3 s after a short pulse of 239 Pu fission is about a factor of two less than that from Pu fissioning under steady state conditions. Furthermore, there are significant energy spectral differences as a function of time after the pulse that must be considered. These and other model dependencies will be discussed in the paper. In the absence of nuclear weapons testing, many of the technical and theoretical challenges of a full nuclear test could be mitigated with a low cost smaller scale 20 ton fiducial mass IBD demonstration detector placed near a pulsed reactor. Potential reactors include the Texas A&M University TRIGA 1 GW–10 ms pulsed facility or the Sandia Annular Core Research Reactor. The short duty cycle and repeatability of pulses would provide critical real environment testing and measurements, which would be valuable for planning a possible real test shot in the future. Furthermore, the antineutrino rate as a function of time data would provide unique constraints on fission databases and model assumptions. Finally, there are impactful science drivers such as sensitive searches for ∼1 eV 2 sterile neutrinos and ∼MeV scale axions.
We explore, using a state-of-the-art simulation code in 3D and late-enough times to witness final observables, the dependence of core-collapse supernova explosions on the nuclear equation of state (EOS). Going beyond questions of explodability, we compare final explosion energies, nucleosynthetic yields, recoil kicks, and gravitational-wave and neutrino signatures using the SFHo and DD2 nuclear EOSs for a 9 M ⊙ /solar-metallicity progenitor star. The DD2 EOS is stiffer and has a lower effective nucleon mass. The result is a more extended protoneutron star (PNS) and lower central densities. As a consequence, the mean neutrino energies, final explosion energy, and recoil kick speed are lower. Moreover, the evolution of PNS convection differs between the two EOS models in significant ways. This translates in part into interestingly altered neutrino “light” curves and noticeably altered gravitational-wave signal strengths and frequency characteristics that may be diagnostic. The faster exploding model (SFHo) yields slightly more neutron-rich ejecta and more species with atomic weights between 60 and 90 and a weak r-process. However, this is merely a preliminary study. The next step is a more comprehensive and multiprogenitor set of 3D supernova simulations for various EOSs to late times when the observables have asymptoted. Such a future investigation will have a direct bearing on the neutron star and black hole birth mass functions and the quest toward a fully quantitative theory of supernova observables.
Calculations for electron capture rates on nuclei with atomic numbers between 𝑍 = 20 and 𝑍 = 52 are performed in a self-consistent finite-temperature covariant energy density functional theory within the relativistic quasiparticle random-phase approximation. Electron captures on these nuclei contribute most to reducing the electron fraction during the collapse phase of core-collapse supernovae. The rates include contributions from allowed (Gamow-Teller) and first-forbidden (FF) transitions, and it is shown that the latter become dominant at high stellar densities and temperatures. Temperature-dependent effects such as Pauli unblocking and transitions from thermally excited states are also included. The new rates are implemented in a spherically symmetric one-dimensional simulation of the core-collapse phase. The results indicate that the increase in electron capture rates, due to inclusion of FF transitions, leads to reductions of the electron fraction at nuclear saturation density, the peak neutrino luminosity, and enclosed mass at core bounce. The new rates reaffirm that the most relevant nuclei for the deleptonization situate around the 𝑁 = 50 and 82 shell closures, but, compared to previous simulations, nuclei are less proton rich. Here, the new rates developed in this work are available, and will be of benefit to improve the accuracy of multidimensional supernova simulations.
Recent models of the rapid proton (𝑟𝑝) capture process indicate that a competition between the 59 Cu(𝑝,𝛾) 60 Zn and 59 Cu(𝑝,𝛼) 56 Ni reactions may result in the formation of a nickel-copper (NiCu) cycle that traps the flux of material between 56 Ni and 60 Zn . Here, in this work, we report the identification of 15 proton-unbound levels in 60 Zn , populated via 59 Cu(𝑑,𝑛) transfer, which govern the rate of the 59 Cu(𝑝,𝛾) 60 Zn reaction in XRBs. Precise excitation energies for levels in 60 Zn were obtained from observed 𝛾 decays, and spectroscopic factors were determined from angle-integrated cross sections. Incorporating these results into stellar-model calculations, we find that with experimentally constrained uncertainties a NiCu cycle in XRBs is indeed possible, though we limit its branching strength to less than 38%. While modest, such a branching has significant impact on the light curve, motivating further studies of the relevant rates. Our calculations also indicate that a significant NiCu cycle leads to an increase in the amount of odd-𝐴 nuclei in the burst ashes, which may affect Urca cooling processes in neutron star crusts.
Electron-capture reactions on iron-group nuclei play a crucial role in the late stages of massive star evolution. Since stellar evolution simulations depend on accurate electron-capture rates—which are highly sensitive to the detailed Gamow-Teller (GT) strength distributions—reliable theoretical models are essential. However, experimental data on GT strength distributions are scarce. High-resolution measurements are therefore vital for benchmarking and improving these theoretical calculations. To provide high-resolution data on Gamow-Teller strength distributions of iron-group nuclei and to compare these results with theoretical calculations within this mass region. Differential cross sections for the 59 Co (𝑡, 3 He) 59 Fe charge-exchange reaction at 115 MeV/u were measured using the S800 spectrometer. Furthermore, to resolve individual levels that are not distinguishable in the S800 particle singles data, coincident 𝛾 rays from the 59 Fe residual nucleus were detected by using the Gamma-Ray Energy Tracking In-beam Nuclear Array 𝛾-ray tracking array. Here, the Gamow-Teller transition strength distribution from the ground state of 59 Co to 59 Fe was extracted up to an excitation energy of 10 MeV. Additionally, transition strengths for several low-lying states were determined from coincident 𝛾-ray measurements. Electron-capture rates calculated using the present data indicate that these low-lying states contribute significantly to the overall rates in relevant stellar environments. The experimental results show reasonable agreement with theoretical predictions based on both shell-model and projected shell-model calculations. High-resolution data on Gamow-Teller strength distributions—particularly for individual low-lying states—are essential for accurately determining electron-capture rates in iron-group nuclei. Coincident 𝛾-ray measurements provide a powerful tool for obtaining such detailed information. While the present work demonstrates that shell-model calculations successfully reproduce the experimental results, such comparisons are scarce and more experimental data are desirable.
Nuclear weak-interaction rates are known to exert a prominent effect in the late stages of stellar collapse. Despite their importance, most studies to date on core-collapse supernovae (CCSNe) have focused primarily on the effects of electron captures, neglecting 𝛽 decay contributions. In this work, we present the first CCSNe simulation incorporating global 𝛽 decay rates from a microscopic theory. These are enabled by a large-scale evaluation of both electron capture and 𝛽 decay rates, obtained self-consistently utilizing the relativistic energy density functional theory and finite-temperature quasiparticle random-phase approximation. Including 𝛽 decay leads to a dramatic enhancement of the pre-bounce antineutrino signal as the antineutrino emissivity increases by more than two orders of magnitude and the luminosity by a factor of 50 relative to thermal emission alone, while the average antineutrino energy increases by over 1 MeV. Furthermore, it is expected that these new rates could help us constrain the model uncertainties related to weak-interaction processes, improving the prediction of antineutrino signal during the final stages of stellar death.
PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.
Distinguishing whether a seismic event is an earthquake or an explosion is a core problem in explosion monitoring. For simplistic models, earthquakes produce a predictable S-wavefield based on their radiation pattern, while an explosion produces no S-wavefield. However, observations from nuclear tests show that explosions can produce significant S-wave energy on both horizontal components. We perform numerical experiments using SPECFEM to constrain when an S-wavefield generated from an explosion might differ from one produced by an earthquake. We generate the S-wavefield for our explosions by placing the source location within a small region consisting of large velocity heterogeneities. For the 2D cases investigated here, a crucial condition is that the size of the region of heterogeneities must be much smaller (approximately 1/12th) than the minimum wavelength of the wavefield for our simulation setups. We investigate both isotropic heterogeneities and anisotropic heterogeneities. Initial results demonstrate that the heterogeneous region lowers the P/S amplitude ratios of an explosion. The presence of strong anisotropic heterogeneities near the source can also produce an S-wavefield with similar amplitudes to the P-wavefield. Lastly, we show that S-wave polarization angles vary as a function of azimuth and distance for explosive sources in the presence of small-scale heterogeneities.
Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).
Detecting radionuclide gas seepage from clandestine underground nuclear tests is central to nonproliferation explosion monitoring research. Yet, early-time (<6 day) gas transport driven by the explosive pressure wave remains poorly constrained due to scarcity of field data. We simulate multi-phase gas transport in the vadose zone using pre-shot data from a recent chemical explosion in P-Tunnel at the Nevada National Security Site, USA. Despite using a simplified 2D-radial model, predictions of tracer arrival matched observations within one order-of-magnitude. Our results show how transient blast forcing rapidly mobilizes gases from the cavity into surrounding rock – critical for optimizing sensor placement and test planning. This unique integration of field data and modeling represents a significant improvement in our ability to predict gas migration from underground explosions. More broadly, it offers insights into the coupled dynamics of pressure waves and contaminant transport in the vadose zone, with implications for monitoring and hazard assessment.
We present the first nuclear cross-section measurements of (p,$\gamma$) and (p,n) reactions on 118 Te at energies relevant for the $\gamma$-process nucleosynthesis. Absolute cross-section values for center-of-mass energies of 6, 7 and 10 MeV are provided, together with a theoretical extrapolation to the Gamow window. This experiment marks the first time that direct proton-induced reactions have been measured on a radioactive ion beam at the Experimental Storage Ring (ESR) at GSI, Darmstadt. This paves the way for a large variety of measurements, delivering new constraints for explosive nucleosynthesis and for physics beyond nuclear stability.
A continuous flow-through, real-time radioxenon sensor array has been designed to monitor either 127 Xe or 133 Xe tracer gas as part of a series of underground chemical explosions in the Low-Yield Nuclear Monitoring (LYNM) Physics Experiment 1 (PE1). The sensors are pairs of shielded thallium-doped sodium iodide gamma-ray detectors that surround a gas measurement chamber. Each measurement chamber is connected to a sampling location in the geology surrounding the chemical explosion or in the access tunnel. The first experiment in the series, PE1 A, occurred in 2023 and the system quantified the 127 Xe tracer gas in the gas sampling lines for four weeks following the experiment execution. The array generated a set of radioxenon concentration time series with a 15-minute resolution that will be used to refine subsurface gas transport models.
Stars are slowly developing objects; the lifetimes of the different burning phases are determined by the strength of nuclear reactions, which in turn are defined by the quantum structure of the associated nuclei at the threshold and the respective reaction mechanisms. Stars, from the nuclear physics perspective, are cold environments where only a few of the key nuclear reactions have been measured at the actual stellar plasma temperatures. This is also the case for more dynamic astrophysical phenomena from the big bang to stellar explosions. Most of the nuclear reaction rates are therefore based on theoretical extrapolations. A number of discrepancies between these predictions and the associated stellar signatures have been observed, and many may be due to low-energy or near-threshold quantum effects. These effects need to be understood in order to reliably model nuclear reaction processes, not only for stars but also for low-temperature plasma environments such as controlled magnetic or inertial confinement fusion systems, which operate in similar temperature regimes. This review summarizes the various theoretical techniques presently used for deriving reaction rates and discusses possible quantum effects that may impact the reaction cross section near the reaction threshold. These resemble enhanced single-particle and cluster structures near threshold and associated interference effects. New experimental techniques such as deep-underground accelerators or the study of transfer reactions to mimic the quantum-mechanical transition strength, the so-called Trojan horse method, provide ways to directly or indirectly probe the reaction features that determine the reaction rates at stellar energies. Furthermore, this is demonstrated on a number of key nuclear reactions for different nucleosynthesis environments. Finally, current inconsistencies between experimental predictions and observations are discussed.
Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.
Core-collapse supernovae, occurring at the end of massive star evolution, produce heavy elements, including those in the iron peak. Although the explosion mechanism is not yet fully understood, theoretical models can reproduce optical observations and observed elemental abundances. However, many nuclear reaction rates involved in explosive nucleosynthesis have large uncertainties, impacting the reliability of abundance predictions. To address this, we have previously developed a Monte Carlo-based nucleosynthesis code that accounts for reaction rate uncertainties and has been applied to nucleosynthesis processes beyond iron. Our framework is also well suited for studying explosive nucleosynthesis in supernovae. In this paper, we investigate 1D explosion models using the ‘PUSH method’ , focusing on progenitors with varying metallicities and initial masses around $M_{\rm ZAMS} = 16\, {\rm M}_{\odot }$. Detailed post-process nucleosynthesis calculations and Monte Carlo analyses are used to explore the effects of reaction rate uncertainties and to identify key reaction rates in explosive nucleosynthesis. We find that many reactions have little impact on the production of iron-group nuclei, as these elements are primarily synthesized in the nuclear statistical equilibrium. However, we identify a few ‘key reactions’ that significantly influence the production of radioactive nuclei, which may affect astrophysical observables. In particular, for the production of ${}^{44}{\rm Ti}$, we confirm that several traditionally studied nuclear reactions have a strong impact. However, determining a single reaction rate is insufficient to draw a definitive conclusion.
Screening and inspection of cargo containers are two essential methods to nondestructively examine the contents of shipment. These methods enable the detection of illicit transportation of unauthorized materials such as nuclear and radioactive materials, explosives, drugs, and so on, typically at borders or secure facilities. Although high-energy X-ray transmission is a standard system and is widely used for cargo inspection, the inherent challenges of high false-positive rates and high attenuation factors necessitate the development of complementary techniques that can increase the detection efficiency and accuracy in large and dense materials. Gamma-rays, which possess higher penetration characteristics because of their high energy, offer an alternative nonintrusive modality for cargo scanning. They represent a promising inspection method when compared to X-rays for three reasons: (1) improved ability to detect nuclear and radioactive materials, (2) higher inspection throughput rates, and (3) lower false-positive rates. Currently, there are two main gamma-ray inspection techniques, active and passive interrogation. Active interrogation can be further grouped into (1) gamma-ray transmission imaging and (2) neutron-induced gamma-ray emission detection. Gamma-ray transmission imaging utilizes differences in material densities for mapping the shipment contents and detecting anomalies. It is analogous to the X-ray transmission method; however, the high-energy photons make it more difficult to shield against, which enables more efficient performance in large and dense material inspection. Neutron-induced gamma-ray emission inspection is designed for the detection of nuclear and radioactive material because those materials emit characteristic gamma-rays when they are activated by neutron absorption. On the other hand, passive interrogation techniques rely on high-efficiency detectors to detect radiation emitted from hidden special nuclear or other radioactive materials. Similar to passive interrogation, cosmic ray muon monitoring and imaging are relatively new techniques that do not require external radioactive sources. These techniques have received attention as a potential next-generation radiographic probe to identify illicit transportation of nuclear and radioactive materials in cargo containers. Cosmic ray muons have unique features, (1) much higher energies than X-rays or gamma-rays (on the order of 10−1—104 GeV), (2) enhanced penetration capability, and (3) natural occurrence, thereby eliminating the need for induced radiation sources. These features enable cosmic ray muons to be utilized for detection of special nuclear materials in high-background-noise environments. By analyzing incoming and outgoing muon trajectories, scattering angles, and energies, it has been shown that it would be possible to locate hidden and well-shielded materials in cargo containers via three-dimensional muon tomography images or signal analysis. Gamma-rays, cosmic ray muons, and other nonintrusive cargo inspection modalities are complementary to each other, allowing them to address various cargo inspection conditions (i.e., scanning time, cost, radiation exposure level, and types of target materials). This chapter presents a detailed review of the theoretical fundamentals and technical principles behind the current gamma-ray and cosmic ray muon modalities for cargo inspection. Additionally, critical assessments and suggestions for the future directions to advance the use of gamma and muon modalities are discussed.
T Coronae Borealis (T CrB) is a symbiotic recurrent nova system expected to undergo its next outburst within the next 2 yr. Recent hydrodynamic simulations have predicted the nucleosynthetic yields for both carbon–oxygen (CO) and oxygen–neon (ONe) white-dwarf models, but without accounting for thermonuclear reaction rate uncertainties. We perform detailed Monte Carlo postprocessing nucleosynthesis calculations based on updated thermonuclear reaction rates and uncertainties from the 2025 evaluation. We quantify the resulting abundance uncertainties and identify the key nuclear reactions that dominate them. Our results show that both the CO and ONe nova models robustly produce characteristic CNO isotopes. More pronounced abundance differences emerge for elements with A ≥ 20. Sulfur is the most robust observational discriminator between the CO and ONe nova models, with a model-to-model difference of a factor of ≈30 and minimal sensitivity to reaction rate uncertainties. Neon, silicon, and phosphorus exhibit even larger abundance differences (factors of ≈150–250), providing strong diagnostic potential. While their predicted yields are subject to larger uncertainties, these remain smaller than the model-to-model differences, allowing these elements to serve as useful, though less precise, tracers of white-dwarf composition. Chlorine, argon, and potassium also differ between models, but the 1σ-abundance ranges for the CO and ONe models overlap, reducing their present usefulness as composition tracers. We find that only nine nuclear reactions dominate the abundance uncertainties of the most diagnostically important isotopes, and their influence is largely independent of the underlying white-dwarf composition. These results provide guidance for future experimental efforts and for interpreting ejecta compositions in the next eruption of T CrB.