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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Absolute Efficiency Characterization of Neutron Detectors within Radioisotope Identification Devices [Slides]

Some Radioisotope Identification Devices (RIIDs) have a low efficiency neutron detector that can provide a neutron singles counting rate along with the primary gamma spectra that can be used to identify a radioisotope. When we know absolute efficiency, other characteristics of a radioisotope can be estimated such as mass or source strength when accompanied by the identity of the isotope and other observable variables. This study focuses on characterizing and comparing the absolute neutron efficiencies of some commonly used RIIDs: the ORTEC Detective X, ORTEC RADEAGLET-R, and FLIR IDentifinder 2.

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
DOE OSTI2025

Radioisotope Identification with List-Mode Gamma-Ray Data

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with spectra containing similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and qualitative distribution analysis. Additionally, we propose a probabilistic classification model that can utilize spectral data, temporal data, or both to determine if the incorporation of temporal information improves radioisotope identification. Our findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated to develop more robust and optimal methods for utilizing this temporal information in applications requiring radioisotope identification.

List-mode data↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

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.

Lalor, Peter W.↗
DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Questionnaire for Radioisotope Identification and Estimation from Gamma Spectra using PyRIID v2

Accurate targeting of radioisotope classifiers and estimators requires an understanding of the target problem space. In order to facilitate clear communication on expected model behavior and performance between practitioners and stakeholders on their problems, this questionnaire was created. Stakeholder responses form the basis of a trained model as well as the start of usage requirements for the model as it is integrated with analysis processes or detection systems. This questionnaire may also be useful to machine learning practitioners and gamma spectroscopists developing new algorithms as a starting point for characterizing their problem space, especially if they are using PyRIID.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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 OSTI2024

Multilabel proportion prediction and out-of-distribution detection on gamma spectra of short-lived fission products

In the machine learning problem of multilabel classification, the objective is to determine for each test instance which classes the instance belongs to. In this work, we consider an extension of multilabel classification, called multilabel proportion prediction, in the context of radioisotope identification (RIID) using gamma spectra data. We aim to not only predict radioisotope proportions, but also identify out-of-distribution (OOD) spectra. We achieve this goal by viewing gamma spectra as discrete probability distributions, and based on this perspective, we develop a custom semi-supervised loss function that combines a traditional supervised loss with an unsupervised reconstruction error function. Our approach was motivated by its application to the analysis of short-lived fission products from spent nuclear fuel. In particular, we demonstrate that a neural network model trained with our loss function can successfully predict the relative proportions of 37 radioisotopes simultaneously. The model trained with synthetic data was then applied to measurements taken by Pacific Northwest National Laboratory (PNNL) to conduct analysis typically done by subject-matter experts. Here, we also extend our approach to successfully identify when measurements are OOD, and thus should not be trusted, whether due to the presence of a novel source or novel proportions.

Anomaly detection↗
DOE OSTI2025

Performance characterization of 5×5×12 mm 3 virtual Frisch-grid TlBr detectors

Thallium bromide (TlBr) is a promising material for semiconductor gamma-ray detectors due to its high atomic number (Tl = 81, Br = 35), high density (7.56 g/cm 3 ), and relatively low melting point (460 °C). These properties give TlBr several competitive features, including high detection efficiency, good energy resolution, and lower fabrication costs, making it suitable for applications requiring compact, highly sensitive gamma-ray detection systems. Recent developments of TlBr detectors have progressed toward thicker crystals, up to 10–20 mm, configured as pixelated or virtual Frisch-grid (VFG) devices with volumes >1 cm 3 . Despite these advances, challenges remain in practical implementation, including material and fabrication issues that affect energy resolution, device stability, performance, and longevity. These challenges are similar to those faced by HgI 2 and lead-halide perovskite detectors. Here, this study presents results from characterizing over 100 position-sensitive 5×5×12 mm 3 virtual Frisch-grid detectors fabricated by Radiation Monitoring Devices Inc. For a prototype Radioisotope Identification Device (RIID). The work identified factors limiting the performance and longevity of TlBr detectors, including stochastic response variations that limit the energy resolution to 2–3 % (at 662 keV) for most devices, performance degradation associated with defect decoration, and contact delamination. Addressing these issues advances the use of TlBr and similar materials for reliable, high-resolution gamma-ray spectroscopy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Special Nuclear Material Mass Estimates from Neutron Singles Count Rate [Poster]

The objective of this research was to create an algorithm to provide an estimate of special nuclear material (SNM) mass using only neutron count rate data from a Radioisotope Identification Device (RIID), rather than using time-correlated data from a neutron multiplicity counter. To meet this objective neutron count rate measurements of a 252 Cf source were taken at varying distances with an ORTEC Detective X, FLIR Identifinder 2, and an ORTEC RADEAGLET-R. An algorithm was created to estimate mass of SNM utilizing the singles rate equation and the measured absolute efficiency curves.

FLIR↗
NASA NTRS1990

Identifying Bearing Balls With Radioisotopes

Proposed scheme for identification of members of manufactured lot of bearing balls based on detection of characteristic isotopes. All balls in lot irradiated to produce easily recognized radioactive isotopes in known concentrations and/or known ratios of concentrations and known rates of decay on their surfaces. Scheme conceived to track precise bearing balls through various stages of assembly, disassembly, and processing.

Butner, Myles F.↗
DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗
NASA NTRS1973

Generation of long time creep data on refractory alloys at elevated temperatures

Four separate studies of various aspects of the vacuum creep behavior of two tantalum alloys (T-111 and ASTAR 811C) and of pure CVD tungsten are reported. The first part of the program involved a study of the influence of high temperature pre-exposure to vacuum or to liquid lithium on the subsequent creep behavior T-111 alloy. Results of this study revealed significant effects of pre-exposure on the 1% creep life of T-111, with life reductions of about 3 orders of magnitude being observed in extreme cases. The second part of this study involved an investigation of the creep behavior of T-111 under conditions of continuously increasing stress and decreasing temperature which simulated the conditions anticipated in radioisotope capsule service. Results of this study showed that such test conditions produced a creep curve having a very unusual shape, and led to the identification of a new creep design parameter for this type of service. The third area of investigation was a study of the influence of heat treatment on the microstructure and creep behavior of ASTAR 811C. The fourth part of the program was directed toward a preliminary characterization of the 1% creep life of CVD tungsten as obtained from two different sources.

Sheffler, K. D.↗
NASA NTRS2017

MapX An In Situ, Full-frame X-Ray Spectroscopic Imager for Planetary Science and Astrobiology

Microbial life exploits micron-scale disequilibria at boundaries where valence, chemical potential, pH, Eh, etc. vary on a length scale commensurate with the organisms - 10's to 100's of microns. The detection of accumulations of the biogenic elements C,N,O,P,S at appropriate concentrations on or in a mineral/ice substrate would constitute permissive evidence of extant life, but context is also required. Does the putative biosignature exist under habitable conditions? Under what conditions of P, T, and chemical potential was the host mineralogy formed? MapX is an in situ robotic spacecraft instrument that images the biogenic elements C, N, O, P, S, as well as the cations of the rock-forming minerals (Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe) and important anions such as Cl, Fl. MapX provides element maps with less than or equal to100 microns resolution over a 2.5 cm X 2.5 cm area, as well as quantitative XRF spectra from ground- or instrument-selected Regions of Interest (ROI). XRF spectra are converted to mineralogies using ground- or instrument-based algorithms. Either X-ray tube or radioisotope sources such as 244Cm (Alpha-particle and gamma- ray fluorescence) can be used. Fluoresced sample Xrays are imaged onto an X-ray sensitive CCD through an X-ray MicroPore Optic (MPO). The MapX design as well as baseline performance requirements for a MapX instrument intended for life detection / identification of habitable environments will be presented.

Astrobiology↗
NASA NTRS1993

The APX Spectrometer for Martian Missions

Obtaining the chemical composition of any planetary body should be a prime science objective of each planetary mission. The APX spectrometer has been designed to provide a detailed and complete chemical composition of all major (except H) and minor elements with high accuracy, in situ and remotely. From such complete analyses a first-order mineralogy of analyzed samples can be deduced. Laboratory studies in the past have shown that rock types (e.g., dunites, basalts, Philippinate 300 sample) were a uniquely in blind test analyses. Such identification is more accurate than can be obtained from any other remote spectroscopic technique. The APX technique is based on three modes of nuclear and atomic interactions of alpha particles with matter resulting in three different energy spectra containing the compositional information. The instrument uses 50 to 100 mCi of Cm-242 or Cm-244 transuranium radioisotopes to provide a monoenergetic beam of alpha particles (6.01 MeV and 5.80 MeV respectively) and solid-state detectors for acquiring the energy spectra.

Economou, T.↗
DOE OSTI2024

Exposed Phosphatidylserine as a Biomarker for Clear Identification of Breast Cancer Brain Metastases in Mouse Models

Brain metastasis is the most common intracranial malignancy in adults. The prognosis is extremely poor, partly because most patients have more than one brain lesion, and the currently available therapies are nonspecific or inaccessible to those occult metastases due to an impermeable blood–tumor barrier (BTB). Phosphatidylserine (PS) is externalized on the surface of viable endothelial cells (ECs) in tumor blood vessels. In this study, we have applied a PS-targeting antibody to assess brain metastases in mouse models. Fluorescence microscopic imaging revealed that extensive PS exposure was found exclusively on vascular ECs of brain metastases. The highly sensitive and specific binding of the PS antibody enables individual metastases, even micrometastases containing an intact BTB, to be clearly delineated. Furthermore, the conjugation of the PS antibody with a fluorescence dye, IRDye 800CW, or a radioisotope, 125I, allowed the clear visualization of individual brain metastases by optical imaging and autoradiography, respectively. In conclusion, we demonstrated a novel strategy for targeting brain metastases based on our finding that abundant PS exposure occurs on blood vessels of brain metastases but not on normal brain, which may be useful for the development of imaging and targeted therapeutics for brain metastases.

Oncology↗