During the last decade, a large number of different numerical methods have been proposed to tackle the automatic identification and quantification in y-ray spectrometry. However, the lack of common benchmarks, including datasets, code and comparison metrics, makes their evaluation and comparison hard. In that context, we propose an open-source benchmark that comprises simulated datasets of various y-spectrometry settings, codes of different analysis approaches and evaluation metrics. This allows us to compare the state-of-the-art end-to-end machine learning with a statistical unmixing approach using the full spectrum. Three scenarios have been investigated: (1) spectral signatures are assumed to be known; (2) spectral signatures are deformed due to physical phenomena such as Compton scattering and attenuation; and (3) spectral signatures are shifted (e.g., due to temperature variation). A large dataset of 2.105 simulated spectra containing nine radionuclides with an experimental natural background is used for each scenario with multiple radionuclides present in the spectrum. Regarding identification performance, the statistical approach consistently outperforms the machine learning approaches across all three scenarios for all comparison metrics. However, the performance of the statistical approach can be significantly impacted when spectral signatures are not modeled correctly. Consequently, the full-spectrum statistical approach is most effective with known or well-modeled spectral signatures, while end-to-end machine learning is a good alternative when measurement conditions are uncertain for radionuclide identification. Concerning the quantification task, the statistical approach provides accurate estimates of radionuclide counting, while the machine learning methods deliver less satisfactory results.

Identifying and quantifying γ-emitting radionuclides, considering spectral deformation from γ-interactions in radioactive source surroundings, present a significant challenge in γ-ray spectrometry. In that context, a hybrid machine learning method has been previously proposed to jointly estimate the counting and spectral signatures of γ-emitters under conditions of spectral variability. This paper addresses the uncertainty quantification of the estimators (i.e., the counting and the variable λ which characterizes the spectral signatures) obtained by this spectral unmixing algorithm. The focus is on the coverage interval, as defined by the GUM, which corresponds closely to a credible interval in the Bayesian framework. Given the inverse problem and the constraints associated with spectral deformation, two Bayesian methods - Laplace approximation and Markov Chain Monte Carlo - have been developed for uncertainty quantification to ensure robust decision-making. The Laplace approximation technique approximates the posterior distribution by a Gaussian distribution, while the Markov Chain Monte Carlo technique samples the posterior distribution. This study evaluates these two methods in terms of precision of coverage interval based on repeated Monte Carlo samples using the long-run success rate. Numerical experiments show that both methods yield similar results close to the expected success rate of 95.4% when constraints related to spectral signatures deformation and counting are inactive. However, when constraints are active or the background counting significantly dominates other radionuclides, the Laplace approximation method deviates from the expected long-run success rate due to the non-Gaussian posterior distribution. In such cases, the Markov Chain Monte Carlo method still provides robust results.

Gamma-ray spectrometry is a widely used technique for identifying and quantifying γ-emitting radionuclides in many nuclear applications. Currently, there is a growing trend to address the problem of automatic identification of radionuclides by implementing machine learning (ML) approaches such as multilayer perceptrons (MLP) or convolutional neural networks (CNN). Alongside these ML methods, the statistical method based on full-spectrum analysis with Poisson likelihood yields reliable results. However, due to the lack of a common benchmark, a comparison of these methods has not yet been conducted. In this work, we introduce a benchmark to evaluate and compare these methods under three scenarios: (1) known spectral signatures, (2) spectra deformed by physical phenomena and (3) gain shift. A large dataset of 200000 simulated spectra was generated for each scenario, spanning a broad range of radionuclide combinations and mixing ratios. This work utilizes a dictionary of nine radionuclide spectral signatures generated with the Monte Carlo code Geant4 for a 3"×3" NaI(Tl) detector, combined with an experimental natural background. For the ML method, we trained a dedicated CNN model for each scenario, carefully optimized its hyperparameters, and adjusted its classification threshold. The results show that the statistical method consistently outperforms the ML approach across all scenarios, particularly under low-statistics conditions. Furthermore, the statistical method maintains a false positive rate close to the predefined level.

Automatic identification and quantification of y-emitting radionuclides, considering the spectral deformation due to y-interactions in the radioactive source environment, is a demanding challenge in the field of nuclear physics. In this context, this paper presents a hybrid unmixing approach combining a pre-trained machine learning model (autoencoder) to capture spectral deformations and a model selection technique based on statistical testing to identify the radionuclides present in a radioactive source and quantify their mixing weights. The identification process of radionuclides is based on a sequential selection algorithm using a likelihood ratio test depending on the expected false positives. Basically, this method aims to minimize the number of radionuclides in an initial radionuclide library containing characteristic y-spectra of each y-emitter to be tested. The robustness of decision-making with this approach and the quantification performance are investigated with Monte Carlo simulations involving up to 12 radionuclides to be tested, according to different mixture scenarios with increasing complexity and various statistics. The false positive rates obtained with the hybrid unmixing approach are close to the expected values. In general, the quantification results are similar to the case when the radionuclides present in the source and spectral signatures are known. This highlights the effectiveness of this novel hybrid unmixing approach for the automatic identification and quantification of gamma-ray emitting radionuclides with spectral variability.

Main text Section III (neutron measurements) of the Comité Consultatif des Rayonnements Ionisants, CCRI, conducted a comparison of primary measurements of the neutron emission rate of a 252 Cf radionuclide source. A single 252 Cf source was circulated to all participants between 2016 and 2020. Ten laboratories participated -CMI (Czech Republic), KRISS (Republic of Korea), IRD/LNMRI (Brazil), LNE-LNHB (France), NIM (China), NIST (USA), NMIJ (Japan), NPL (UK), NRC (Canada) and VNIIM (Russia) - with NPL making their measurements at the start and repeating them at the end of the exercise to verify the 250 Cf content of the source. Each laboratory reported the emission rate into 4π sr together with a detailed uncertainty budget. All participants used the manganese bath technique except NMIJ who used a relative method based on measurements with a 3 He detector in a graphite pile. VNIIM also made measurements using an associated particle technique. CMI, KRISS, LNE-LNHB, NIM, NPL and VNIIM also measured the anisotropy of the source although this did not formally form part of the comparison. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Neutron spectrometry plays a crucial role in decommissioning of nuclear sites and in ensuring the safety of workers from radiation exposure. In this study, we associate a proton recoil spectrometer made by a homemade plastic scintillator with triple discrimination capabilities (fast neutrons, thermal neutrons, and gamma rays) with deconvolution methods to obtain accurate measurements of neutron spectra. The purpose of this work is to test and evaluate the performance of three distinct deconvolution codes, namely MAXED (Maximum Entropy Deconvolution), GRAVEL, and MLEM (Maximum Likelihood Expectation Maximization), using two types of input spectra (flat and Watt spectrum). These deconvolution codes are applied to simulated data using the reference MCNP6.2 Monte Carlo code. Comparing the calculated mean squared error (MSE) performed by the three unfolding methods, we find that MLEM seems to perform better than MAXED and GRAVEL. Furthermore, given the calculated MSE values, the unfolded spectrum of Cf-252 is in better agreement with the standard reference spectrum by using Watt spectrum as input spectrum (MSE<1.2×10-6) than using a flat spectrum (MSE<1.3×10-6).

Automatic identification and quantification of γ-emitting radionuclides, taking into account spectral deformations due to γ-interactions in radioactive source surroundings, is a challenging task in the nuclear field. In that context, this paper presents a Machine Learning approach based on autoencoder that can learn a model for the spectral signatures of γ-emitters with variability. Training and test datasets were obtained by means of simulated γ-spectra computed with the Geant4 simulation code according to increasing material thicknesses (steel, lead). A novel hybrid unmixing algorithm combining a pretrained autoencoder is studied for joint estimation of spectral signatures and counting in the case of mixtures of four radionuclides (57Co, 60Co, 133Ba, 137Cs). The investigations were carried out to account for spectral deformations due to attenuation, Compton scattering and fluorescence at high and low statistics. This study demonstrates the validity of this novel hybrid approach combining Machine Learning and Maximum Likelihood for the automatic full-spectrum analysis of γ-spectra.

The automatic identification and quantification of gamma-emitting radionuclides, accounting for spectral deformation due to gamma interactions in the environment of radioactive sources, is a challenge for various nuclear applications. In this paper, this problem is addressed by developing a hybrid approach combining Machine Learning and classical statistical methods. A specific Machine Learning-based autoencoder that can capture spectral variability with limited data is proposed. A novel hybrid unmixing algorithm employing a pre-trained autoencoder is investigated for joint estimation of spectral signatures and counting in the case of mixtures of four radionuclides (Co-57, Co-60, Ba-133, Cs-137). This study was carried out to take account of spectral deformation due to attenuation and Compton scattering at low statistics. The results demonstrate the validity of this new hybrid approach based on Machine Learning and Maximum Likelihood for the automatic full-spectrum analysis of gamma-spectra.

A bilateral comparison to determine the activity concentration of the same 125I solution was organized. As electron-capture radionuclide with a rather high atomic number, 125I must be regarded as difficult to measure. The situation is partly exacerbated by the fact that some established standardization methods, like photon-photon coincidence counting, can no longer be applied due to the unavailability of appropriate equipment and expertise. One aim of this work is to compare modern liquid scintillation counting methods for the standardization of 125I. Both participating metrology institutes have used their custom-built triple-to-double-coincidence ratio (TDCR) counters and the determined activity concentrations are in excellent agreement even though the ways to analyze the data and to compute counting efficiencies were widely independent. The results also agree with the outcome of 4π-γ counting that was carried out at LNHB. In both laboratories, the measurements were complemented by measurements with several secondary standardization methods which even allow to establish a link to the CCRI(II)-K2.I-125(2) comparison started in 2004. A good agreement between the TDCR results and the key comparison reference value of the 2004/2005 comparison was obtained.

Main text A bilateral comparison between LNE-LNHB, France and the RC POLATOM, Poland on activity measurements of the 51Cr solution was organized in 2022 as the EURAMET project No 1554 and registered as EURAMET.RI(II)-K2.Cr-51 key comparison. In both laboratories, primary activity measurements techniques based on 4π(LS)-γ coincidence and anti-coincidence counting methods were used. LNE-LNHB also performed indicative measurements in a large-well NaI(Tl) detector using the 4πγ method. The final result of the comparison in RC POLATOM was the weighted mean of the results of the 4π(LS)-γ coincidence method and the 4π(LS)-γ anti-coincidence method, and in the LNE-LNHB - the value obtained by the 4π(LS)-γ anti-coincidence method. The power-moderated mean of the final results values was calculated. An additional ampoule containing the same 51 Cr solution was sent to the BIPM in order to link the results achieved in the bilateral comparison to the BIPM SIR reference system. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Abstract Introduction Commissioning, calibration, and quality control procedures for nuclear medicine imaging systems are typically performed using hollow containers filled with radionuclide solutions. This leads to multiple sources of uncertainty, many of which can be overcome by using traceable, sealed, long-lived surrogate sources containing a radionuclide of comparable energies and emission probabilities. This study presents the results of a quantitative SPECT/CT imaging comparison exercise performed within the MRTDosimetry consortium to assess the feasibility of using 133Ba as a surrogate for 131I imaging. Materials and methods Two sets of four traceable 133Ba sources were produced at two National Metrology Institutes and encapsulated in 3D-printed cylinders (volume range 1.68–107.4 mL). Corresponding hollow cylinders to be filled with liquid 131I and a mounting baseplate for repeatable positioning within a Jaszczak phantom were also produced. A quantitative SPECT/CT imaging comparison exercise was conducted between seven members of the consortium (eight SPECT/CT systems from two major vendors) based on a standardised protocol. Each site had to perform three measurements with the two sets of 133Ba sources and liquid 131I. Results As anticipated, the 131I pseudo-image calibration factors (cps/MBq) were higher than those for 133Ba for all reconstructions and systems. A site-specific cross-calibration reduced the performance differences between both radionuclides with respect to a cross-calibration based on the ratio of emission probabilities from a median of 12–1.5%. The site-specific cross-calibration method also showed agreement between 133Ba and 131I for all cylinder volumes, which highlights the potential use of 133Ba sources to calculate recovery coefficients for partial volume correction. Conclusion This comparison exercise demonstrated that traceable solid 133Ba sources can be used as surrogate for liquid 131I imaging. The use of solid surrogate sources could solve the radiation protection problem inherent in the preparation of phantoms with 131I liquid activity solutions as well as reduce the measurement uncertainties in the activity. This is particularly relevant for stability measurements, which have to be carried out at regular intervals.

This work presents a new system dedicated to the measurement of energy spectra from radionuclide-based neutrons sources (ex. AmBe, PuBe, 252 Cf). The experimental device consists of a large-volume polyethylene container that is equipped with a central channel accommodating the source to be measured and 12-measurement channels (in a spiral formation) around the source, into which detectors can be placed. The container is filled with water in order to moderate neutrons emitted from the source and to reduce the sensitivity of the detectors to the external environment. Measurements have been performed with 6 Li-doped plastic scintillators (PS) developed in-house and optimized for the simultaneous detection of fast neutrons, thermal neutrons, and gamma rays, through signal processing based on Pulse Shape Discrimination (PSD). This novel approach solves the underdetermined problem as observed in the case of classical neutron spectrometers (ex. Bonner sphere systems with limited experimental data). Through the use of 6 Li-doped PS information, the increased experimental data enable the spectrum of interest to be recreated without the need to introduce a "default spectrum" in the unfolding process. The reconstruction is performed with an iterative Maximum-Likelihood Expectation–Maximization algorithm (ML-EM) throughout the detector's responses matrix calculated with the MCNP6 code. An optimized version of this ML-EM algorithm with a regularization step has been also tested. The design, methodology and preliminary results are presented. Different types of neutron sources were measured and results are consistent, with a clear benefit when using experimental data taking into account the measured fast neutron component instead of using only thermal neutron counting.

Atomic and nuclear data represent an important input for the accuracy of primary activity measurements based on liquid scintillation. In particular, the reliability of β-spectrum computation has been investigated for several years through experimental and theoretical studies providing solid evidence for the need to consider the atomic effects. In the present study, the activity standardization of two β-emitting radionuclides (60Co, 106Ru/106Rh) was carried out by means of the 4πβ−γ coincidence and Triple-to-Double Coincidence Ratio (TDCR) methods. The comparison between the activity concentrations given by both primary techniques presents new evidence that a better agreement is obtained when the exchange and screening effects are included in the β-spectra implemented in the model of light emission for TDCR measurements. A new development of a stochastic model based on Geant4 simulations for TDCR calculations is also presented.

Signal unmixing is a class of complex, ill-posed inverse problems, which includes blind source separation or underdetermined signal separation to cite only two. Retrieving signals from their mixtures generally relies on adapted representations allowing to disentangle them. When dealing with real-world scientific data, the main challenge is to further build meaningful signal representations, which generally means capturing the underlying low-dimensional manifold structure of the signals to be recovered. Since the latter is generally unknown, this calls for a learning-based approach, which is a challenging task, especially when available training samples are scarce. The objective of the paper is to investigate a new learning model to build low-dimensional signal representations from few training samples. Based on an encoder-decoder architecture, the proposed approach aims to learn a non-linear interpolating scheme from examples. Extensive numerical experiments have been carried out to evaluate the performances of the proposed approach. We further illustrate how the learned representations can be conveniently deployed to tackle challenging semi-blind unmixing problems in the field of γ-ray spectroscopy.

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides. The exercise input files and calculation results with practical recommendations are made available for new users on a dedicated webpage.

Main text Since 1976, 13 laboratories have submitted 27 samples of 139Ce to the International Reference System (SIR) for activity comparison at the Bureau International des Poids et Mesures (BIPM), with comparison identifier BIPM.RI(II)-K1.Ce-139. Recently, the NMISA (South Africa) and the LNE-LNHB (France) participated in the comparison and the key comparison reference value (KCRV) has been updated. The degrees of equivalence between each equivalent activity measured in the SIR and the updated KCRV have been calculated and the results are given in the form of a table. A graphical presentation is also given. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Nuclear waste management is a major challenge for the nuclear industry. Radiological characterization of nuclear waste packages is an important step of this industrial process as an inaccurate analysis could lead to the over classification of a package and, consequently, to a higher cost of storage. In this context, INNOWTECH and CEA List develop a low-cost and automated nuclear measurement method based on gamma spectrometry carried out with CdZnTe detectors in view of reducing uncertainties on radionuclide activities. In a previous study, we showed that the MCNP6 model of the GR1+ KROMEK CdZnTe detector created by Selivanova et al. (Appl. Radiat. Isot., 2019) contains non-modelled parts and inaccurate parameters that contribute to reducing the validity range of the simulation model. In this paper, we carry out an optimization study of such a Monte-Carlo simulation model using an original approach. First, we assess experimentally the detector’s response according to the angular distribution in metrological conditions at LNHB (Laboratoire National Henri Becquerel, France). Secondly, we conducted a comparative study of different gamma spectrometry softwares and retain the sIGAle code developed by CEA List. Thirdly, we fine-tuned the MCNP6 simulation model. On the one hand, the geometry of the model is adjusted thanks to gamma radiographies of the CdZnTe detector conducted with a pixelated CdTe detector (Widepix system) and an 241 Am source. On the other hand, a tool named SBINI (Simulation-Based Inference for Nuclear Instrumentation) is used to infere physical parameters of the MCNP6 model in order to optimize simulation results according to experimental ones. This new approach based on two complementary tools can be applied to optimize Monte-Carlo simulation models of various particle detectors.

Main text To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Metrological non-destructive characterization of intense (α, n) neutron sources (emission rate between 107 and 3×108 s−1 into 4π sr solid angle) is a challenge for nuclear applications. In this article, we will present the design, development and in situ validation of two measurement cells especially dedicated to this characterization. The main goal consists in determining the absolute neutron emission rate of this type of neutron sources with relative uncertainties less than 5% (k=1). Another purpose is related to the qualitative and quantitative analysis of fission products present in irradiated neutron sources. Because of experimental constraints, acquisitions were carried out underwater which required a full remote control of the measurement cells. Neutron emission rate is assessed using three different non-destructive techniques: (i) global neutron counting, (ii) neutron activation using gold dosimeters and (iii) gamma-ray spectrometry. We will successively present the design step and associated constraints, main characteristics of measurement cells and associated laboratory characterization as well as experimental results obtained during the final in situ measurement campaign. The latter shows an excellent agreement regarding the neutron emission rate values provided by the three different non-destructive techniques and a relative combined standard uncertainty less than 5% (k=1) for the investigated neutron sources.