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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.
High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced X-rays and gamma-rays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signal-to-background ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and half-lives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.
This paper will discuss the application of gamma spectroscopy systems for analysis of environmental media samples for parents and daughter progeny of the Natural Uranium and Thorium Decay Series radionuclides. The concentrations and relative ratios of naturally occurring isotopes are used to confirm the presence of FUSRAP-related wastes and for understanding the associated potential dose and health risks. Focus will be given to lessons learned from the set-up and operation of onsite gamma spectroscopy laboratories used to provide stakeholders with defensible and timely results to support FUSRAP site investigations and remediation including adaptive survey design implementation, physical remediation and waste transport, and confirmatory final status survey activities. This paper focuses on the potential impacts to gamma spectroscopy measurement accuracy from the following: - Bias caused by sample matrix effects as well as use of different library reference values between performance evaluation study providers and testing laboratories; - Use of calibration sources with limited energy ranges, and; - Selection of available alternative isotopic decay reference data for the input of line energies and their intensities. These lessons learned include discussing how inter-laboratory measurement differences from blind performance evaluation samples are affected by the manufacturer's measurement method, including equilibrium assumptions; the isotope decay information source library used by the study provider and participating laboratories; and calibration source geometry/density variances from actual sample conditions. Any of these factors can significantly impact the accuracy of reported results data. Deepening the reader's understanding of the various factors that impact the accuracy, interpretation, and overall usability of gamma spectroscopy results data from laboratories will help ensure defensible decision-making and cost-effective mission implementation within FUSRAP and similar remediation programs. (authors)
This article discusses experimental approaches for the development of gamma spectroscopy well logging system. Considering the size of borehole sonde, we customize 2 x 2 inches inorganic scintillators and the system including high voltage, preamplifier, amplifier and multichannel analyzer (MCA). The calibration chart is made by test using standard radioactive sources so that the measured count rates are expressed by energy spectrum. Optimum high-voltage supplies and the measurement parameters of each detector are set up by experimental investigation. Also, the responses of scintillation detectors have been examined by analysis according to the distance between source and detector. Because gamma spectroscopy well logging needs broad spectrum, high sensitivity and resolution, the energy resolution and sensitivity as a function of gamma ray energy are investigated by analyzing the gamma ray activities of the radioactive sources.
The purpose of this administrative procedure is to describe the analysis of gamma spectroscopy data. This procedure specifies how gamma spectroscopy data are used to identify and quantify radioactivity in sample waste items.
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.
This technical report describes testing to evaluate the gamma spectroscopy tool, Melusine, under development by Pacific Northwest National Laboratory. The goal was to verify that the software can successfully be used to provide accurate results and statistical uncertainties for the detection of isotopes of interest and their activities. Of special interest were spectra similar to those produced by radionuclide stations that contribute to the Comprehensive Nuclear Test Ban Treaty Organization’s International Monitoring System. Two data sets were used to test Melusine’s capabilities. The first was the result of a multi-lab calibration effort based on neutron activations produced at the University of California at Davis. The second was taken from the Proficiency Test Exercises conducted by the CTBTO directly in 2005. In 37 of 42 cases, Melusine produced results in agreement with the best answer presently available, in most cases with calculated uncertainties comparable to or better than competing analyses. In fact, Melusine technically provided one more result than CTBTO’s PTE analyses that agreed with the “book answer” (Monte Carlo simulation). Despite these promising results, the Melusine software is still under development. Effort is especially needed to simplify its analysis process, improve stability, and provide user documentation. Some significant analysis tasks require further vetting, such as those to address summing effects. However, our test results indicate that Melusine’s calculations as presently implemented are sound and can be used to reliably analyze spectra from the CTBTO’s radionuclide stations.
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.
High resolution gamma spectroscopy (HRGS) is a widely used technique for non-destructive characterization of nuclear materials, and assay of radioactive waste. The ability to accurately evaluate raw spectral data is an important step in producing high-quality results. A spectral analysis application known as Peak Doctor has been developed under a Cooperative Research and Development Agreement (CRADA) between LANL and Pajarito Scientific Corporation (PSC). The software has undergone several years of testing and validation by the Radioactive Waste Characterization Team of the LANL Waste Management Services Group (WM-SVS). PeakDoctor is designed to work with the Spectral Nondestructive Assay Platform (SNAP{sup TM}) to provide a complete HRGS analysis engine. SNAP{sup TM} performs model specific corrections for attenuation and geometry. These applications are the major components of a new toolkit designed primarily for portable far-field non-destructive assay. Peak Doctor performs a very accurate peak fit analysis with the goal of improving upon the capabilities of existing commercially available routines. It is designed to be user-configurable and can be customized to any given type of detector and any given type of spectrum. Peak Doctor was developed to resolve many of the inherent complexities in gamma ray spectra including overlapping peaks, complex multiplet regions, large step peaks, backscatter regions and Compton edges. Unlike most spectral analysis platforms, Peak Doctor does not rely on the energy calibration and detector resolution parameters that are saved by the operator in the spectrum file. Instead, key parameters are pre-defined for individual detectors. The software fits a continuum using a series of splines between a series of junctions or 'knots'. Fitting techniques are used to join the splines together to create a near-continuous multi-element curve. Optimizing the location of the knots is one of Peak Doctor's most powerful features. The knots are initially configured using a pre-defined list and are removed or added automatically during the fitting process. Knots are also created when new features are identified such as Step Peaks and Compton Edges. The user can also manually specify knots in order to improve the continuum fit. The initial fit is improved on by removing any outlier regions (which are converted to peaks) and the continuum fitting process is repeated. At this stage, an experienced user can work interactively with the fit by manually converting peak regions to continuum or vice versa. The final step is to fit a series of peaks above the continuum based on detector parameters. Single and multiplet peaks are then processed by either the tailing (piecewise) or non-tailing (Gaussian) Levenberg Marquardt non-linear peak fitting routine depending on the user defined selection. Only a rough knowledge of the detector characteristics (energy calibration and resolution) is required for the fitting process to proceed with good accuracy. Any peaks that are missed can be manually added by the user and will also be fitted. Discontinuities in the peak / continuum fit can be rapidly identified by reviewing the 'residuals scale' plot. For waste streams that are well characterized, it is often the case that routinely encountered spectra will have similar peaks and continuum features. For these cases a handy 'One Step' method is available that proceeds in an automated fashion without user interaction. Peak Doctor has been configured with a set of default spectrum types including plutonium / transuranic waste (including Weapons Grade Pu, Heat Source Pu, Am-241, Am-243 and Np-237), depleted or enriched uranium, nuclear power plant / intermediate level waste (with fission and activation products such as Cs-137 and Co-60) as well as Naturally Occurring Radioactive Materials (NORM). The user can easily add new spectrum types for specific waste streams or other types of radiological / special nuclear materials. The output from PeakDoctor (including net peak area, centroid energy and a list of minimum detectable activity levels at pre-defined regions) is used by SNAP{sup TM} to complete the data analysis process. Isotope activities are calculated using configurable template-driven geometry and attenuation correction methods. The software accounts for matrix and container wall effects, collimator / angular corrections, detector efficiency (based on calibration curves) and stand-off distance. SNAP{sup TM} uses published algorithms incorporating a series of decision points during its operation to allow the analyst to provide logical input to guide the software during the analysis of each unique spectrum. The Peak Doctor / SNAP{sup TM} toolkit has been extensively tested and validated under a variety of conditions using 'benchmark' reference spectra. Test scenarios reported include HPGe-based measurements of well characterized uranium and plutonium standards and radioactive sources with a variety of different geometries and matrix materials including surrogate drums and boxes. (authors)
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.
International nuclear safeguards use nondestructive assay techniques to verify declared characteristics of spent nuclear fuel (SNF) assemblies. The declared characteristics usually include the initial enrichment, assembly average burnup, and irradiation history. The primary nondestructive assay tool used by the International Atomic Energy Agency has been the fork detector irradiated fuel measurement system, which uses gross gamma and neutron correlations; however, there have been several research publications within the last decade on the use of high-resolution gamma spectroscopy for the determination of SNF characteristics. References [1]–[6] are published journal articles on this subject from 2006–2016. This report describes measurements of 25 individual SNF rods using high-resolution gamma spectroscopy. Details of the measurement geometry, measurement settings, and rod characteristics are documented. These spectra are intended for the validation of simulation codes that calculate SNF inventory and source term characteristics. The spectrum archive is available to evaluate future spectrum analysis methods. The archive contains full axial scan spectra of each SNF rod. This allows more detailed comparison between simulation and nondestructive analysis than is possible on assemblies.
The standard operating procedure is described for in situ high-resolution gamma spectroscopy, including operational readiness, planning, preparation, conduct, and reporting. Data analysis of in situ gamma spectrum files will be performed together with those of other gamma assay methods by a dedicated Data Analysis process, documented in companion OSI procedures.
Transmutation of {sup 64}Zn to {sup 65}Cu has been observed in a ZnO crystal irradiated with neutrons. The crystal was characterized with electron paramagnetic resonance (EPR) before and after the irradiation and with gamma spectroscopy after the irradiation. Major features in the gamma spectrum of the neutron-irradiated crystal included the primary 1115.5 keV gamma ray from the {sup 65}Zn decay and the positron annihilation peak at 511 keV. Their presence confirmed the successful transmutation of {sup 64}Zn nuclei to {sup 65}Cu. Additional direct evidence for transmutation was obtained from the EPR of Cu{sup 2+} ions (where {sup 63}Cu and {sup 65}Cu hyperfine lines are easily resolved). A spectrum from isolated Cu{sup 2+} (3d{sup 9}) ions acquired after the neutron irradiation showed only hyperfine lines from {sup 65}Cu nuclei. The absence of {sup 63}Cu lines in this Cu{sup 2+} spectrum left no doubt that the observed {sup 65}Cu signals were due to transmuted {sup 65}Cu nuclei created as a result of the neutron irradiation. Small concentrations of copper, in the form of Cu{sup +}-H complexes, were inadvertently present in our as-grown ZnO crystal. These Cu{sup +}-H complexes are not affected by the neutron irradiation, but they dissociate when a crystal is heated to 900 °C. This behavior allowed EPR to distinguish between the copper initially in the crystal and the copper subsequently produced by the neutron irradiation. In addition to transmutation, a second major effect of the neutron irradiation was the formation of zinc and oxygen vacancies by displacement. These vacancies were observed with EPR.
The radiation resulting from neutron interactions with Ge nuclei in active volume of HPGe detectors is one of the main concerns in low-level gamma spectroscopy measurements [1,2]. It is usually not possible to measure directly spectrum of neutrons which strike detector. This paper explore the possibility of estimation of neutron spectrum using measured activities of certain Ge(n,γ) and Ge(n,n’) reactions (obtained from low-level gamma measurements), available ENDF cross section data and unfolding procedures. In this work HPGe detector with passive shield made from commercial low background lead was used for the measurement. The most important objective of this study was to reconstruct muon induced neutron spectrum created in the shield of the HPGe detector. MAXED [3] and GRAVEL [4] algorithms for neutron spectra unfolding were used. The results of those two algorithms were compared and we analyzed the sensitivity of the unfolding procedure to the various input parameters.
Inverse radiation transport is a well-established method for determining unknown parameters within a system that exhibits particle leakage. Two significant challenges of solving inverse transport problems are (1) to determine multiple unknowns without false convergence on local minima and (2) to be able to quantify uncertainty. It was recently demonstrated that the Differential Evolution Adaptive Metropolis (DREAM) method effectively meets both of these challenges. In Bledsoe's 2014 article, the DREAM method was only applied to numerical test cases in which measured data were simulated using MCNP. This paper provides results of research conducted at Oak Ridge National Laboratory (ORNL) in which DREAM was applied to two-dimensional inverse transport problems using real-world measurements. The DREAM method uses a Markov chain Monte Carlo (MCMC) method to obtain a posterior distribution of unknown parameters. In this application, the posterior distribution is determined by using a uniform prior distribution and a likelihood function. The goal of the inverse problem is to find the regions in which the likelihood function is at or near its maximum. In the traditional MCMC approach, a single Markov chain is employed. In practice, MCMC approaches have a high acceptance rate to solutions that are near the current point of sampling, but they exhibit slow convergence to solutions far from the sampling point. The DREAM algorithm was designed to alleviate this issue by initiating multiple Markov chains and using the differential evolution algorithm to generate trial points for each chain. Solving for the mass of radioactive material within a voluminous source is a challenging problem for gamma-ray spectroscopy. Inverse gamma-ray transport can supply a range of possible material compositions, but reducing the differences between predicted and actual measurements can lead to false results. DREAM is a rapid technique for determining the most likely solution for several unknown parameters in an inverse transport problem. This work applies DREAM to a series of gamma spectroscopy experiments performed at ORNL.
The FRAM gamma-ray isotopic analysis code has recently been applied to four Pu data sets and four U data sets, each with relatively well-characterized standard items, in order to estimate the variances of random errors and of item-specific systematic errors. Assay results are available for Pu238, Pu239, Pu240, Pu241, Pu242 (operator entered, not independently measured), Am241, Specific Power, and Pu240eff. For the four U data sets, results are available for the measurands U234, U235, U236 (operator entered, not independently measured), and U238. For example, in Pu data set 1, there are 33 standard reference Pu items, which have well-known nominal/true values (except for a few items). FRAM was used to make approximately 15 repeat measurements for each of the 33 items, so empirical standard deviations can be calculated. In addition, FRAM internally estimates its random error variance using propagation of variance. This paper applies analysis of variance (ANOVA) to separately estimate the repeatability standard deviation and the standard deviation of the item-specific biases δ s in each of the 8 data sets.