The ScIDEP Collaboration is constructing muon telescopes based on scintillator technology to investigate the internal structure of the Egyptian Pyramid of Khafre at Giza near Cairo using cosmic-ray muons. The collaboration aims to scan the pyramid from multiple viewpoints, both inside the King's burial chamber that is located centrally at the base of the pyramid, and outside of the pyramid, to potentially identify any new internal structures. An overview of the project is presented, including the development of the data-acquisition system, the simulation framework, and very first detector studies.

A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.

Scintillator detectors are an integral component of radiation detection systems for a variety of applications such as medical imaging, accelerator diagnostics, and space science. Typically, a scintillator detector’s response is characterized using gamma sources to understand the detection response to different types of radiation, including charged particle detection. However, there exists a non-linearity of the amount of light produced from an incident gamma-ray of specific energy and the light produced from an incident charged particle of the same energy. This important effect, known as quenching, must be accounted for to interpret energies from charged particles incident on detectors. In this paper, we present results of quenching parameterization for two types of cerium-doped inorganic scintillators, Y 2 SiO 5 :Ce (YSO:Ce) and Gd3Al 2 Ga 3 O 12 :Ce (GAGG:Ce). We measured the light output from incident proton energies from 1 MeV to 25 MeV using a 3 MV tandem accelerator and two reactions: Au (p,p) Au and 3 He( d,p ) 4 He. Using gamma-ray sources to calibrate the detectors, we compared the measured electron-equivalent energy versus the incident energy expected. Using an adaptation of the Birks semi-empirical formula, we extracted the Birks parameter (kB) to understand quenching. For one of the GAGG:Ce samples, the kB parameter of 0.0072 [g cm -2 MeV -1 ] is comparable to a similar study where the value of kB was 0.0065 [g cm -2 MeV -1 ]. For YSO:Ce, no other kB values were found in the literature. Three different types of GAGG:Ce were used to collect measurements of kB as a function of dopant concentration.

<p>Dry cask spent fuel storage containers currently house much of the world’s spent nuclear fuel stores outside of spent fuel cooling pools. In order to maintain, continuity of knowledge of these containers, seals are applied. When these seals are broken, the containers must be relocated to spent fuel pools for visual inspection.  Large amounts of steel shielding, required for radiation safety, significantly attenuates both radiation emitted from the fuel itself and incoming radiographic probes such as x-ray and neutrons. As a result, the effectiveness of these more traditional radiographic probes is greatly diminished, and no other passive, in situ verification methods are currently in use. While promising results have been demonstrated, long measurement times limit the attractiveness of cosmic-ray muon radiography as a passive verification method. The work presented here compares performance of differing reconstruction techniques to draw recommendations for the optimization of future cosmic-ray muon tomography measurements. For tomographic imaging, the results show that using a plenoptic depth of field reconstruction method, rather than traditional backprojections, results in better imaging resolution for a limited number of views. The depth of field reconstruction method also requires a smaller number of views to reconstruct images useful in the verification of dry cask spent fuel storage containers.</p>

to triplet quenching. The nature of the high-concentration effect shown by Group I is analyzed. Some results obtained with mixtures of anions support the conclusion that triplet AQS is also responsible for this effect and it is suggested that triple exciplexes of the type /sup 3/(AQS/sup -/.X/sub 2//sup -/) are involved. With this view and the recently proposed intra-radical-spin-orbit-coupling (IRSOC) model, a quantitative interpretation of the results is presented.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the advantage actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20×20 m convex and non-convex simulation environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

Machine learning is increasingly applied for gamma-ray spectra analysis, particularly gamma-ray identification. Radionuclide identification is a multiclass multilabel classification problem which can be tackled with methods including artificial neural networks, extreme gradient boosted tress (XGBoost) and Random Forest. In this study, we compare the performance of these classifiers using the Benchmark Algorithm for Radionuclide Identification (BARNI) as a framework for processing raw spectra into pre-selected features extracted using a peak search algorithm. Sampled spectra from a 3”x3” NaI detector with a library of 33 radionuclides under a spanning set of shielding configurations was used for training and testing. The overall performance of each classifier was assessed using the F1-score. We also break down the per nuclide performance, and demonstrate which classifiers are better for identification of particular radionuclides. The experimental results show that XGBoost was the optimal choice when performance and training time was valued. The Multi-Layer Perceptron (MLP) neural network showed potential as a runner up although lacked the proper amount of optimization and tuning in order to surpass the XGBoost classifier. The Random Forest classifier was the weakest perform in both performance and training time. over the total number of samples present in each data set. The x-axis displays the actual nuclides while the y-axis shows the predicted nuclides. The diagonal of the confusion matrix contains the prediction fraction and is the fraction of correctly predicted nuclides. While the off diagonals are where the nuclides got confused with a another nuclide. The none category is where there was a false negative or where none of the nuclides were detected.

Many radionuclide identification algorithms use statistical inference to collect a variety of features from gamma-ray spectra to deduce the presence of particular radionuclides. More modern algorithms require large amounts of data to learn and use latent features from spectra for classification. Both approaches are computationally expensive, which is reflected in their power consumption, and require large amounts of user intervention to prepare. In this article, we introduce a low-power, neuromorphic algorithm for the real-time identification of radionuclides which simultaneously considers the entire shape of a gamma-ray spectrum. Utilizing the output of a traditional gamma-ray detector, our spiking, locally competitive algorithm uses sparse coding optimization to compare global patterns in a gamma-ray spectrum with a dictionary of radionuclide templates. This approach allows us to model informative global features resulting from both photoelectric absorption and Compton scattering. For the purpose of radiation threat reduction, the dictionary consists of data from the Nuclear Wallet Cards, a list of radionuclides and their properties compiled by the National Nuclear Data Center. To test our algorithm, we use a variety of gamma-ray spectra created using radionuclides measured under laboratory conditions with varying durations, distances, activity levels, and backgrounds, resulting in a wide range of signal-to-noise ratios. We have created test sets for three different gamma-ray detector types, with 57 Co, 137 Cs, 152 Eu, 60 Co, 239 Pu, and 235 U sources, to quantify the effect of resolution, efficiency, and background on the accuracy of the algorithm. We demonstrate a true positive accuracy of 91% with a high-resolution detector and 89% with a low-resolution detector on the corresponding test sets. Experimenting with the same radionuclides included in the test sets in a variety of special nuclear material (SNM) masking configurations, we show that our algorithm is capable of correctly identifying both SNM and mask even when the activity level of the mask is several times higher than that of the SNM. We also determine that our algorithm achieves over a 99% reduction in power consumption over other radionuclide identification software applications, which is critical for long-term, independent monitoring and is the goal of this research.

Fission products contain a wealth of information on the fission process, but typically one dimensional data is extracted, such as a mass or energy distribution. Highly correlated data is valuable for better understanding of the fission process, and is in demand for fission theory. Correlated fission data sets were taken on the University of New Mexico (UNM) Fission Spectrometer using dedicated detectors, analog electronics, and digital data acquisition. Timing module detectors and an axial ionization chamber made for the spectrometer were employed to provide the raw signal pulses. Analog signal processing treated the detector signals and performed fast time comparisons to extract product time of flight and ionization chamber drift time. These signals, combined with the anode and gamma ray detector signals, were put through the digitizer, and pulse heights and time stamps were recorded. This allowed us to set coincidence windows for combining the data into event packets corresponding to individual fission products. From the coincidence events we are able to extract velocity, kinetic energy, mass, information on atomic number, and coincident gamma rays, particle-by-particle from ejected fission products.

Radioactive aerosols that arise from natural sources and nuclear accidents can be a long-term hazard to human health. Despite the heterogeneous particle deposition in the respiratory tract, uniform aerosol doses have long been assumed in respiratory radiation dosimetry predictions, such as in the compartment and uniform distribution models. It is unclear how these deposition patterns affect internal radiation doses, which are critical in the health assessment of radioactive hazards. This work seeks to quantify the radio-dosimetry sensitivity to initial deposition patterns by comparing computational and compartment/uniform models. A new approach was developed to implement the compartment model into voxel phantoms (e.g. VIP-man) for radiation dosimetry. The calculated radiation fluence, energy deposition density and organ doses were compared to those obtained from coupling computational fluid-particle dynamics (CFPD) with Monte Carlo radiation transport and to those obtained from uniform source distribution approximation. The results show that the source particle distribution within the respiratory system substantially influences the radiation dosimetry distribution. The compartment and uniform models underestimated aerosol deposition in the crania ridge, leading to lower doses in the trachea and surrounding organs. For 0.5 MeV gammas, the CFPD-Monte Carlo N-particle (MCNP) model predicted a tracheal dose twice that of the compartment model and four times the uniform model. For 1 MeV betas, the CFPD-MCNP-predicted tracheal dose is 2.6 times that of the compartment model and 14 times the uniform model. Compared to the compartment/uniform models, the CFPD approach predicted a 50% lower beta dose in the lung but higher beta doses in the heart (six times), liver (four times) and stomach (2.5 times). It is suggested that including compartments for the lung periphery and tracheal carina ridge may improve the dosimetry accuracy of compartment models.

Radioactive materials ionize air surrounding them and produce molecules such as ozone and nitrogen oxides. Concentrations of these species above background can serve as a marker for radioactive materials and provide a means for stand-off detection of nuclear materials. Ozone is one of the primary radiolytic products in air and has a lower background level than oxides of nitrogen, so it is a molecule of interest for this study. Actinides of interest include Pu-239 and U-235, which are strong alpha emitters which have a high linear energy transfer in air. Ozone yield, saturation, and decay time are dependent on creation and destruction processes which change with charge density, ozone density, radiation dose rate, and type of radiation, motivating measurements of ozone yield and saturation level from alpha radiation with dose rates more typical of actinide sources. As a stand in for Pu and U, studies were performed on ozone yield in air from the alpha particle emitter Po-210. The sample was enclosed in a 0.96 +/- 0.01 L Teflon cylinder and both air flow and build-up measurements were performed, with air flow of similar to 1 L/min and build up times to 3966 min corresponding with 2640 Gy. Build up measurements showed an ozone concentration increase at short times (t <= 10 min) with a yield of G(O-3) = 7.7 +/- 0.3 molecules/100 eV. Air flow measurements showed a radiation chemical yield for ozone of G(O-3) = 7.6 +/- 0.2 molecules/100 eV. These yields are larger than in previous, very old alpha particle studies but consistent with low rate gamma and electron beam studies. To use ozone as a secondary marker of special nuclear materials the saturation level in still air is important. The extracted ozone saturation level in the cylinder is 1.35 ppm with a half life of 74 +/- 3 min.

The University of New Mexico Fission Spectrometer was developed to measure fission product yield, as part of the LANL SPIDER collaboration. The spectrometer operates as an E-v detector to extract product mass event-by-event, with a time of flight region followed by an ionization chamber for kinetic energy measurements. By using the ionization chamber as a singlecathode/single-anode time projection chamber, stopping power and thus Z information is extracted, for coupled A and Z measurements. New work is being performed to add gamma ray detectors in the data stream, placed near the target region for prompt gammas and near the ionization chamber for quasiprompt (>50 ns) and later gammas, correlated with individual fission products. A stand-alone parallel plate ionization chamber (PPIC) is also being developed for fission tagging gamma ray data. The PPIC will also allow discrimination between charged particle out events and (n,n’γ), and discriminate between alpha emission and fission. Using layers in the PPIC, other targets can be measured simultaneously with a calibration target, giving relative fission cross sections. Past measurements with the spectrometer were performed at LANSCE and we plan to continue measurements there. The current work is supported by the NNSA Stewardship Science Academic Alliance.

Understanding the propagation of radiation damage in a material is paramount to predicting the material damage effects. To date, no current literature has investigated the Threshold Displacement Energy (TDE) of Ca and F atoms in CaF2 through molecular dynamics and simulated statistical analysis. A set of interatomic potentials between Ca-Ca, F-F, and F-Ca were splined, fully characterizing a pure CaF2 simulation cell, by using published Born-Mayer-Huggins, standard ZBL, and Coulomb potentials, with a resulting structure within 1% of standard density and published lattice constants. Using this simulation cell, molecular dynamics simulations were performed with LAMMPS using a simulation that randomly generated 500 Ca and F PKA directions for each incremental set of energies, and a simulation in each of the [1 0 0], [1 1 0], and [1 1 1] directions with 500 trials for each incremental energy. MD simulations of radiation damage in CaF2 are carried out using F and Ca PKAs, with energies ranging from 2 to 200 eV. Probabilistic determinations of the TDE and Threshold Vacancy Energy (TVE) of Ca and F atoms in CaF2 were performed, as well as examining vacancy, interstitial, and antisite production rates over the range of PKA energies. Many more F atoms were displaced from both PKA species, and though F recombination appears more probable than Ca recombination, F vacancy numbers are higher. The higher number of F vacancies than Ca vacancies suggests F Frenkel pairs dominate CaF2 damage.

Despite extensive efforts in studying radioactive aerosols, including the transmission of radionuclides in different chemical matrices throughout the body, the internal organ-specific radiation dose due to inhaled radioactive aerosols has largely relied on experimental deposition data and simplified human phantoms. Computational fluid-particle dynamics (CFPD) has proven to be a reliable tool in characterizing aerosol transport in the upper airways, while Monte Carlo based radiation codes allow accurate simulation of radiation transport. The objective of this study is to numerically assess the radiation dosimetry due to particles decaying in the respiratory tract from environmental radioactive exposures by coupling CFPD with Monte Carlo N-Particle code, version 6 (MCNP6). A physiologically realistic mouth-lung model extending to the bifurcation generation G9 was used to simulate airflow and particle transport within the respiratory tract. Polydisperse aerosols with different distributions were considered, and deposition distribution of the inhaled aerosols on the internal airway walls was quantified. The deposition mapping of radioactive aerosols was then registered to the respiratory tract of an image-based whole-body adult male model (VIP-Man) to simulate radiation transport and energy deposition. Computer codes were developed for geometry visualization, spatial normalization, and source card definition in MCNP6. Spatial distributions of internal radiation dosimetry were compared for different radionuclides (131I, 134,137Cs, 90Sr-90Y, 103Ru and 239,240Pu) in terms of the radiation fluence, energy deposition density, and dose per decay.

Gas ionization detectors are very widely used for radiation detection and measurement, but for external sources an entrance window is used which can reduce particle energy. For heavy ions this energy loss can be significant enough to affect measurements and very thin windows, such as those composed of silicon nitride (SiN), may be utilized to minimize this effect. For fission spectroscopy, carbon conversion foils are also common in measurements. In the current work, energy losses were measured for Cf-252 spontaneous fission products passing through thin foils of carbon and of silicon nitride. The foils ranged in from 22.5 to 131.0 mu g/cm(2) for C and from 56.4 to 402.2 mu g/cm(2) for SiN. For comparison, simulations were performed with the TRIM program in SRIM-2013 and with MCNP6.2. To understand calculation differences, effective charge from partial ionization was predicted by several methods and compared with results directly using the Bethe stopping power formula.