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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Deployment Gamma Instruments Laboratory Exercise

The instructional goals for this session are to provide participants with a thorough understanding of gamma-ray spectroscopy techniques and to familiarize them with the operation and performance of the NCV-owned gamma-ray instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Segmented Gamma Scanner for Radioactive Waste Assessment: A User Guide

Radioactive waste assessment is important for ensuring nuclear material security at various types of facilities, such as enrichment, fuel fabrication, and reprocessing plants. The waste generated at such nuclear facilities is stored in standard containers and is required to be characterized for material-accounting purposes. The segmented gamma scanner system is a popular, nondestructive analysis measurement system used for characterizing nuclear material, including radioactive waste. This document provides guidance on how to achieve effective performance from a segmented gamma scanner system for accurately quantifying fission products, activation products, and transuranic wastes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Multimodal Data Fusion with 3D Gamma-Ray Imaging for Safeguards (2023 Annual Report)

Improve the quantitative results obtained from 3D gamma-ray imagers for use in safeguards inspections. In FY23 the work focused on continuing to improve the numerical results obtained with gamma-ray imagers and explore approaches to project such data onto 3D structures mapped with Self Localization and Mapping (SLAM) hardware and software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Advanced Test Reactor Loop Activity Report: Gamma-Ray Emitting Radionuclides Concentrations and Decontamination Factors of ATR Loop Liquid Samples for ATR Cycle 175B-1

The gamma-ray emitting radionuclide concentration and decontamination factor results from gamma-ray spectrometry measurements of ATR loop liquid samples by the Radiation Measurements Laboratory (RML) are contained in this report for ATR Cycle 175B-1 from August 4, 2025 to October 6, 2025.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Advanced Test Reactor Loop Activity Report: Gamma-Ray Emitting Radionuclides Concentrations and Decontamination Factors of ATR Loop Liquid Samples Cycle 173C-1

The gamma-ray emitting radionuclide concentration and decontamination factor results from gamma-ray spectrometry measurements of ATR loop liquid samples by the Radiation Measurements Laboratory (RML) are contained in this report for ATR Cycle 173C-1 from November 22, 2024, to January 21, 2025.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Quantification of LEU Holdup using gamma ray imaging and inverse transport solver

Holdup is the residual amount of special nuclear material (SNM) remaining in a processing facility after the bulk materials have been cleaned out. In commercial uranium processing facilities, quantification of holdup is a major challenge because of the highly variable shapes and sizes of the deposits. Any method that attempts to generalize and calibrate deposit shapes in order to quantify holdup will be prone to high uncertainties. Uncertainties on the order of ±50% are typical in holdup results. In international safeguards applications, a ±50% uncertainty can result in a large amount of material unaccounted for (MUF) thereby increasing the difficulty of detecting material diversion and facility misuse. An imaging-based methodology has been developed with the objective of significantly reducing this uncertainty by using the true deposit shape, instead of relying on oversimplified geometric assumptions. The project is a collaboration between ORNL, Y-12, and the University of Tennessee, Knoxville, TN. Uranium sources of known masses were measured using the Germanium Gamma-ray Imager (GeGI), a high-resolution imaging spectrometer, creating a pixelated map for each spectral bin. Two different gamma imaging methods are employed in this work: coded aperture imaging and Compton imaging. A validated MonteCarlo model of the detector has been developed using the GEANT4 code for determining the intrinsic response of the detector, its enclosure, and the coded aperture mask. An inverse transport solver based on the Markov Chain Monte-Carlo approach known as Differential Evolution Adaptive Metropolis (DREAM) is employed to use the measurement data from the image pixels (coded aperture or Compton) to solve for the mass of 235 U in the deposit. A reliable method based on the DREAM solver has been developed to flag the infinite thickness condition of a uranium deposit. The project team is working towards improving the image reconstruction for Compton imaging so that a better localization of the source can be achieved. Besides treating the coded aperture and Compton imaging methods independently, the project is also evaluating a combined method that uses the Compton scatter data from a coded aperture measurement. GEANT4 simulations are being performed to evaluate the combined approach. The impact on the DREAM optimization as the source thickness progressively approaches infinite thickness is being evaluated. A number of uranium sources available at ORNL have been measured, and the DREAM results have been tested and validated for the coded aperture imaging. A similar effort will be carried out to validate the Compton based method once the development of algorithms for better localization are complete. The imaging based quantification is very amenable to unattended monitoring of holdup accumulation at key measurement points. A proof of concept measurement has been completed to demonstrate this capability The current work used the high energy resolution imager GeGI. However, the approach and methodologies are applicable to other imagers such as the cadmium zin telluride (CZT) based imager manufactured by H3D, Inc.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

GADRAS + Geant4 Simulated Gamma Spectra Dataset

This dataset contains GADRAS and Geant4 simulated gamma-ray energy spectra used in the papers "Sim-to-real supervised domain adaptation for radioisotope identification" (https://doi.org/10.1016/j.nima.2025.171159) and "Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy" (https://arxiv.org/abs/2603.05719). Each spectrum contains a mixture of several different radioisotopes, and the goal of the analysis is to leverage domain adaptation techniques to use a model trained on source-domain spectra (GADRAS) to classify target-domain spectra (Geant4).

Lalor, Peter William [Pacific Northwest National L↗
DOE OSTI2022

Monte Carlo Simulation and Reconstruction: Assessment of Myocardial Perfusion Imaging of Tracer Dynamics With Cardiac Motion Due to Deformation and Respiration Using Gamma Camera With Continuous Acquisition

Purpose: Myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) is routinely used for stress testing in nuclear medicine. Recently, our group extended its potential going from 3D visual qualitative image analysis to 4D spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration and the estimated myocardial blood flow (MBF) and coronary flow reserve (CFR). However, the quality of reconstructed image is compromised due to cardiac deformation and respiration. The work presented here develops an algorithm that reconstructs the dynamic sequence of separate respiratory and cardiac phases and evaluates the algorithm with data simulated with a Monte Carlo simulation for the continuous image acquisition and processing with a slowly rotating SPECT camera. Methods: A clinically realistic Monte Carlo (MC) simulation is developed using the 4D Extended Cardiac Torso (XCAT) digital phantom with respiratory and cardiac motion to model continuous data acquisition of dynamic cardiac SPECT with slowly rotating gamma cameras by incorporating deformation and displacement of the myocardium due to cardiac and respiratory motion. We extended our previously developed 4D maximum-likelihood expectation-maximization (MLEM) reconstruction algorithm for a data set binned from a continuous list mode (LM) simulation with cardiac and respiratory information. Our spatiotemporal image reconstruction uses splines to explicitly model the temporal change of the tracer for each cardiac and respiratory gate that delineates the myocardial spatial position as the tracer washes in and out. Unlike in a fully list-mode data acquisition and reconstruction the accumulated photons are binned over a specific but very short time interval corresponding to each cardiac and respiratory gate. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it continuously deforms. These results are then compared with the conventional 4D spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. Mean Stabilized Activity (MSA), signal to noise ratio (SNR) and Bias for the myocardium activities for three different target-to-background ratios (TBRs) are evaluated. Dynamic quantitative indices such as wash-in (K1) and wash-out (k2) rates at each gate were also estimated. Results: The MSA and SNR are higher with higher TBRs while biases were improved with higher TBRs to less than 10%. The correlation between exhalation-inhalation sequence with the ground truth during respiratory cycle was excellent. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Estimated values of K1 and k2 were also consistent with the ground truth. Conclusion: The continuous image acquisition for dynamic scan using conventional two-head gamma cameras can provide valuable information for MPI. Our study demonstrated the viability of using a continuous image acquisition method on a widely used clinical two-head SPECT system. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Precise implementation of reconstruction algorithms, better segmentation techniques by generating images of different tissue types and background activity would improve the feasibility of the method in real clinical environment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Postbiotic Gamma-Aminobutyric Acid and Camel Milk Intervention as Innovative Trends Against Hyperglycemia and Hyperlipidemia in Streptozotocin-Induced C57BL/6J Diabetic Mice

Diabetes is a serious disease that threatens human health worldwide. The study hypothesis is to investigate the novel trends that may aid in the prevention of diabetic complications. Camel milk was presented as traditional functional food, and Lactobacillus brevis KLDS 1.0727 and KLDS 1.0373 strains were shown to synthesize postbiotic Gamma-aminobutyric acid as a potential food additive, which can therapeutically intervene against hyperglycemia and hyperlipidemia in streptozotocin-induced C 57 BL/6J mice. During a four-week timeframe, body weight and postprandial blood glucose levels were monitored. Post-euthanasia, blood plasma was obtained to investigate hyperlipidemia, insulin concentrations, liver, and renal functions. The liver, pancreas, kidney, and spleen underwent histopathological examinations. The results demonstrated that KLDS 1.0727 and KLDS 1.0373 ( LAC S1 , LAC S2 ) and camel milk treatments all had a significant influence on hypoglycemic activity, as evidenced by reduced postprandial blood glucose levels. LAC S1 , LAC S2 , and camel milk therapy significantly reduced blood hypolipidemic, and some liver enzymes such as (alanine aminotransferase and aspartate transaminase) levels. Therefore, we recommend consuming camel milk regularly and expanding its use with fermented foods containing L. brevis , one of the probiotics capable of producing gamma-aminobutyric acid (GABA) as future food additives that can improve human health and reduce the prevalence of several diseases disorders.

Abdelazez, Amro↗
DOE OSTI2022

Rice Bran Stabilisation and Oil Extraction Using the Microwave-Assisted Method and Its Effects on GABA and Gamma-Oryzanol Compounds

Rice bran oil (RBO) is a valuable ingredient extracted from rice bran (RB), a side stream of polishing rice grain in the milling process. RBO is rich in bioactive ingredients with potential health benefits, such as gamma-oryzanol (GO) and gamma-aminobutyric acid (GABA). Despite its benefits, the quality of RBO depends on the degree of stabilisation of the RB, which is easily affected by lipase enzymes, and thus needs an effective treatment prior to RBO production. To assess the potential of the microwave-assisted method for RB stabilisation and RBO extraction, three Carolino rice varieties (Ariete, Teti, Luna) were tested. The effect of RB stabilisation was evaluated via acid value, water absorption, and GO and GABA levels. The RBO yield was optimised by solvent, temperature, and solvent-to-sample ratio, and the GO and fatty acid levels were determined. The RB stabilisation for the Luna variety did not affect the GO and GABA; for the Ariete and Teti varieties, the GO decreased by 34.4% and 24.2%, and the GABA increased by 26.5% and 47.0%, respectively. The GO levels in RBO samples were not affected by RB stabilisation. The RBO nutritional value was confirmed by the suitable ratio (>2) between polyunsaturated (PUFA) and saturated fatty acids (SFA), with the Teti variety presenting the highest ratio.

Reis, Núria (ORCID:0000000194339109)↗
DOE OSTI2021

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Bilton, Kyle J. (ORCID:0000000184553689)↗
DOE OSTI2021

A New Sample of Gamma-Ray Emitting Jetted Active Galactic Nuclei—Preliminary Results

We are compiling a new list of gamma-ray jetted active galactic nuclei (AGN), starting from the fourth catalog of point sources of the Fermi Large Area Telescope (LAT). Our aim is to prepare a list of jetted AGN with known redshifts and classifications to be used to calibrate jet power. We searched in the available literature for all the published optical spectra and multiwavelength studies useful to characterize the sources. We found new, missed, or even forgotten information leading to a substantial change in the redshift values and classification of many sources. We present here the preliminary results of this analysis and some statistics based on the gamma-ray sources with right ascension within the interval 0 h --12 h (J2000). Although flat-spectrum radio quasars and BL Lac objects are still the dominant populations, there is a significant increase in the number of other objects, such as misaligned AGN, narrow-line Seyfert 1 galaxies, and Seyfert galaxies. We also introduced two new classes of objects: changing-look AGN and ambiguous sources. About one third of the sources remain unclassified.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Neutrino Production Associated with Late Bumps in Gamma-Ray Bursts and Potential Contribution to Diffuse Flux at IceCube

IceCube has detected many TeV–PeV neutrinos, but their astrophysical origins remain largely unknown. Motivated by the observed late-time X-ray/optical bumps in some gamma-ray bursts (GRBs), we examine the correlation between IceCube neutrinos and GRBs allowing delayed neutrinos ~days after the prompt gamma-rays. Although we have not found any definitive correlation, up to ~10% of the events observed so far at IceCube may have been neutrinos produced by the late-time GRB activities at ~1 day. Assuming a connection between some IceCube events and the late GRB bumps, we show in a model-independent way that GRB sites capable of producing late ~PeV neutrinos should be nonrelativistic or mildly relativistic. We estimate the diffuse neutrino flux from such sources and find that they can possibly account for a few IceCube events. Future observations of high-energy neutrinos and late-time GRB afterglows can further test the above proposed connection.

79 ASTRONOMY AND ASTROPHYSICS↗