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Gamma Spectroscopy, or the identification of radio-isotopes based on energy, is extraordinarily useful in the Nuclear field. High Purity German (HPGe) gamma detectors along with Sodium Iodine (NaI) gamma detectors are commonly used to identify possible contamination and determine enrichments, among many other applications.
For many years Lawrence Livermore National Laboratory (LLNL) has used gamma spectroscopy to determine quantities of radioactive nuclides created by irradiation of stable species with neutrons and light charged particles. LLNL has also maintained activation codes and nuclear cross sections for the purpose of modeling activations under the experimental conditions, and the cross section sets used in those calculations typically include nuclear isomers. Connecting the measurements to activation calculations that include isomers is a non-trivial matter because an activation calculation must be followed by a procedure for bookkeeping of calculated isomer populations after the irradiation to simulate what radiochemists would infer about isotope populations from gamma spectroscopy performed days or weeks after the irradiation. Traditionally we have used the method of “isomer sums” for this bookkeeping. This report explains our formulation of isomer sums and application to specific cross section sets for different nuclides that were detected.
Independent gamma spectroscopy data analysis of a plutonium oxide sample was requested on July 23, 2021. The primary request was to assess the Pu-239 activity/mass of the sample using previously collected gamma spectral data. Using the provided gamma spectral analysis report and spectral files, an independent evaluation of the data was conducted without any prior knowledge of the isotopic activity/mass of the sample.
The C-NR Countroom analyzes gamma spectroscopy data using three codes, SAP (which is the combination of SPECANAL and RAYGUN), GAMANAL and UNISAMPO. The codes use libraries which are chosen by the user of gamma emission data to do the identification and quantification. The Countroom maintains nine unique libraries for use in analysis. Historically, the values contained in these libraries has not been consistently updated and many values are quite old and do not reflect the state-of-the-art.
Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.
This report documents results assessing the presence of Naturally Occurring Radioactive Material (NORM) in illicit drugs using high-resolution gamma spectroscopy. Specifically, high-purity germanium (HPGe) measurements were conducted at the Drug Enforcement Administration (DEA) South Central Laboratory on 11 samples (referred to as exhibits) including cocaine, fentanyl, heroin, ketamine, LSD, marijuana, methamphetamines, psilocybin mushrooms, and synthetic cannabinoids.
This report documents results for a scoping study evaluating the viability of using the higher energy, low yield U-235 gamma emission at 345.9 keV to link low energy U-235 emissions to high energy U-238 emissions (rather than U-232 decay products gamma emissions) for highly enriched uranium isotopic gamma spectroscopy relative efficiency assessments when no or little U-232 is present and the U-235 signal is strong.
In 2006, the United States Congress mandated for the Department of Homeland Security (DHS) to screen all cargo containers to protect against terrorist acts and ensure the safety and security of the nation. Containers are screened by gamma and neutron detectors to ensure threat material is not smuggled into the country. However, because commerce is radioactive, detecting the presence of radioactive materials is not sufficient in ensuring the safety and security of the nation. Radioactive materials must also be identified in real time, thus distinguishing threat sources (Uranium-235 and Plutonium-230) from non-threat sources (kitty litter, pot ash, medical isotopes). Screening cargo containers can be considered a two-step process (1) alerting to the presence of radioactive material when gamma counts exceed a threshold setting, and (2) once alerted, identifying the type of radioactive material, which is done by collecting a gamma spectra and analyzing it with an analysis tool/algorithm. For this reason, it is important to evaluate not only emerging technology in neutron and gamma detection, but also investigate new advances in algorithm development for radioisotope identification (RIID). New candidates in detection and on-board algorithm analysis might offer opportunities to make the scanning, detection, and identification process more efficient while still ensuring the health and safety of the public. This research will investigate emerging technology in radiation detection focused on gamma spectroscopy capabilities and RIID algorithms for DHS applications.
Neutron-rich Pd-119 nuclei were produced in fission of natural uranium, induced by 25-MeV protons. Fission fragments swiftly extracted with the Ion Guide Isotope Separation On-Line method were mass separated using a dipole magnet and a Penning trap, providing mono-isotopic samples of Pd-119. Their beta(-) decay was measured with gamma gamma- and beta gamma-spectroscopy methods using low-energy germanium detectors and a thin plastic scintillator. Two distinct nuclear-level structures were observed in Ag-119, based on the 1/2(-) and 7/2(+) isomers reported previously. The beta(-)decay work was complemented by a prompt-gamma study of levels in Ag-119 populated in spontaneous fission of (252)cf, performed using the Gammasphere array of germanium detectors. Contrary to previous suggestions, our data show that the 1/2(-) isomer is located below the 7/2(+) isomer and is proposed as a new ground state of Ag-119 with the 7/2(+) isomer excitation energy determined to be 33.4 keV. Our data indicate that there are two beta unstable isomers in Pd-119, a proposed ground state of Pd-119 with tentative spin 1/2(-) or 3/2(+) and a half-life of 0.88 s and the other one about 350 keV above, having spin (11/2(-)) and a half-life of 0.85 s. The higher-energy isomer probably decays to the 1/2(-) or 3/2(+) ground state via a gamma cascade comprising 18.7-219.8-X-keV transitions. The unobserved isomeric transition with energy X approximate to 100 keV probably has an E3 multipolarity. Its hindrance factor is significantly lower than for analogous E3 isomeric transitions in lighter Pd isotopes, suggesting an oblate deformation of levels in Pd-119. Oblate configurations in Ag-119 are discussed also.
Presentation for Northern New Mexico College RCT students.
Anticipated growth in the number and complexity of nuclear facilities entering the decommissioning phase of their lifespan requires innovative methods of mitigating radiological and safety hazards for workers. Advancements in robotics provide opportunities for remote characterization of radiologically contaminated ducts and pipes, reducing worker exposure. However, the nuclear decommissioning industry has shown a hesitancy in adopting new technology due to concerns regarding capital costs and a lack of demonstration data in representative environments. This work aims to leverage novel soft robotics to develop and demonstrate a sensor-integrated characterization tool for contaminated ducting systems. Two gamma spectrometers – one scintillation-based and one solid-state – were selected and integrated into a pneumatically-actuated snake-like soft robot using stretchable electronic cabling. The robot was then demonstrated at the Idaho National Laboratory’s Critical Infrastructure Test Range Complex. The robot maneuvered through a pseudo-constrained corridor containing several sealed sources and the integrated spectrometers collected spectra at various points along the path of travel. Both spectrometers successfully collected spectra with identifiable, isotope-specific peaks. To evaluate the effect of contamination buildup on the collected spectra, the robot with the integrated scintillation detector was tested using a liquid radioactive source. The resulting spectra showed little detection interference from the liquid contamination; however, a short-lived isotope was used which may not be representative of longer-lived contamination.
Accurate knowledge of the total mass of molten salts both for pyroprocessing spent nuclear fuels and for molten salt reactors is necessary for safeguards purposes. However, it is challenging to know or measure the total mass of molten salt due to the complicated shapes as well as the compositional—and thus density—changes that occur over time during operation. Here, we investigated radioactive tracer dilution (RTD) as a potential safeguards technique for application to the total mass measurement of LiCl–KCl–UCl 3 salt used for uranium electrorefining, with a focus on the feasibility, uncertainty, and fission product effect of RTD for salt mass measurement. To this end, experimental-scale RTD tests (600–700 g of total mass) were initially carried out by adding 22 Na tracer salt into LiCl–KCl salt containing 0, 5, and 28wt% radioactive salts from an electrorefiner (ER) for refining uranium. Upon the completion of the experimental-scale RTD tests, we performed a scale-up RTD test by directly adding 22 Na tracer salts to the ER, which uses about 12 kg of LiCl–KCl–UCl 3 salt during normal operation. This paper reports the main features of RTD for total mass measurement of molten salt and summarizes the results of the experimental-scale and scale-up RTD tests.
Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.
The High Efficiency and Resolution Microcalorimeter Spectrometer, 400 pixels (HERMES-400) is an ultra-high resolution gamma-ray spectrometer at Idaho National Laboratory’s Materials and Fuels Complex. HERMES-400 is intended to enable precise nondestructive analysis of advanced fuel cycle materials. Key upgrades and repairs of the instrument were performed this year to improve performance. First, the fourth and final microcalorimeter detector module was installed, meeting the milestone for its integration into the system. Operation with all four modules enables higher detector efficiency than any previous microcalorimeter gamma-ray system. Second, a thinner magnetic shield was installed. This shields the sensitive microcalorimeter pixels from stray magnetic fields while allowing transmission of lower-energy gamma rays, which are important to isotopic analysis. Finally, the cryostat, which provides cooling of the modules to their operating temperature, was found to have a helium coolant leak, which was repaired. HERMES-400 is in a stable state to perform repeated, long-term gamma-ray measurements with good detector efficiency and energy resolution. In FY25, irradiated TRISO fuel and salts from advanced electrochemical recycling techniques are candidates for microcalorimeter measurements. A final upgrade of the magnetic shielding and vibration damping of the system is also planned to maximize the system performance.
Photon branching ratios are critical input data for activities such as nuclear materials protection and accounting because they allow material compositions to be extracted from measurements of gamma-ray intensities. Uncertainties in these branching ratios are often a limiting source of uncertainty in composition determination. Here, we use high statistics, high resolution - (60–70 eV full-width-at-half-maximum at 100 keV) gamma-ray spectra acquired using microcalorimeter sensors to substantially reduce the uncertainties for 11 plutonium ( 238 Pu, 239 Pu, 241 Pu) and 241 Am branching ratios important for material control and accountability and nuclear forensics in the energy range of 125 keV to 208 keV. Lastly, we show a reduction in uncertainty of over a factor of three for one branching ratio and a factor of 2–3 for four branching ratios.
Twenty-five high-burnup pressurized water reactor used fuel rods were scanned in the Irradiated Fuels Examination Laboratory at Oak Ridge National Laboratory with a high-resolution gamma spectrometer. A planar high-purity germanium detector was used across an energy range from 60 keV to 3 MeV. Gamma spectra of individual fuel rods were collected in 4.5 mm steps along the entire length of each rod. Additional spectra were collected on select rods in rotational increments of 30 degrees to provide data on the distribution of fission products along the rod perimeter.
Radiation energy is measured in units of electron volts (eV). Photon energies are like a fingerprint that allow us to identify specific radioisotopes. Radiation interacts with our detector & deposits energy. That deposited energy produces a proportional signal. Signals are sorted and analyzed. Signals are placed in a histogram, making a gamma spectrum. Analyze gamma spectrum and identify the radioactive material.
This experiment demonstrated that some hard-to-detect fission products can be quantified directly from mixed fission product samples using advanced radio-emission spectroscopy techniques. A gamma/gamma coincidence detector was combined with a custom liquid scintillation counter to evaluate the benefits of alpha and beta coincident and anti-coincident analysis of mixed fission product samples. Reduction factors in the minimum detectable activity (MDA) relative to traditional gamma counting were quantified for a set of radionuclides. For isotopes like 91Y, 144Ce, 147Nd, and 153Sm MDA reductions of factors of 1-2 were observed using optimized beta thresholds. Using beta anti-coincidence counting reductions of factors 3-5 were observed for isotopes like 89Sr, 95mNb, 115Cd, and 137Cs and factors of 5-7 for isotopes like 235U using alpha coincidence counting. The selectivity of gamma/gamma coincidence and reduction in backgrounds using beta gating were also demonstrated with 136Cs and 153Sm. The improvements observed in the detection sensitivity of fission products and actinides achieved by combining time-correlated radiation signatures is significant and of value to applications such as nuclear fission physics, environmental monitoring for nuclear waste, reactor accidental releases, and nuclear forensics.