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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗
DOE OSTI2024

Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
NASA NTRS2016

Emissions Relationships Among Western Forest Fire Plumes: II. Plume Typing for Sources of Ozone and Aerosol Absorption

Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAs SEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing Cburn Ctot added to the fire plume, where Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individualspecies i, xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.

ozone↗
NASA NTRS2017

Emissions Relationships Among Western Forest Fire Plumes: I. Emission Factors Free from Mixing Errors

Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAsSEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing C burn = Δ Ctot added to the fire plume, where ≈ Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individual species i, δέλτα-xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AÅE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.

plumes↗
NASA NTRS2015

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate Which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(sub burn) = delta C(sub tot) added to the fire plume, where C(sub tot) approximately equals (CO2 + CO). Mixed-effects regression can estimate pre-fire background values of Ctot (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta epsilon lambda tau alpha−x(sub i)/(C(sub burn))i,j., MERET and "consensus" require more than two emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (bscat, babs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics.

fires↗
NASA NTRS2016

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(burn) = delta C(tot) added to the fire plume, where C(tot) approximately equals (CO2 = CO). Mixed-effects regression can estimate pre-fire background values of C(tot) (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta, epsilon lambda tau alpha-x(sub I)/C(sub burn))I,j. MERET and "consensus" require more than emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (b scant, b abs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics

ozone↗
DOE OSTI2025

Speciation mapping of the oxidation layer on aged uranium dioxide using scanning transmission x-ray spectromicroscopy

In this study, UO 2 was aged in humid air and prepared as a thin section using a focused ion beam (FIB) instrument. The specimen was measured using synchrotron radiation spectromicroscopy techniques at the Beamline 11.0.2 STXM end station of the Advanced Light Source (ALS). Non-negative matrix factorization (NMF) methods were used to identify and map three component x-ray absorption near-edge structure (XANES) spectra in the oxygen K-edge data, revealing a surface layer of U 4 O 9 with a thickness of 206 ± 21 nm, and the bulk of the sample remaining as UO 2 . Uranium N 4,5 -edge XANES spectromicroscopy supports these results. The diffusion-controlled parabolic rate constant for UO 2 oxidation to U 4 O 9 was calculated from the observed layer thickness and compared to literature values. Complementary transmission electron microscopy (TEM) was used to image the sample and identify the phases present in various regions, confirming the STXM results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Robust quantification of the diamond nitrogen-vacancy center charge state via photoluminescence spectroscopy

Nitrogen vacancy (NV) centers in diamond are at the heart of many emerging quantum technologies, all of which require control over the NV charge state. Hence, methods for quantification of the relative photoluminescence intensities of the NV 0 and NV − charge states, i.e., a charge state ratio, are vital. Several approaches to quantify NV charge state ratios have been reported but are either limited to bulk-like NV diamond samples or yield qualitative results. We propose an NV charge state quantification protocol based on the determination of sample- and experimental setup-specific NV 0 and NV − reference spectra. The approach employs blue (400–470 nm) and green (480–570 nm) excitation to infer pure NV 0 and NV − spectra, which are then used to quantify NV charge state ratios in subsequent experiments via least squares fitting. We test our dual excitation protocol (DEP) for a bulk diamond NV sample and 20 and 100 nm nanodiamond particles and compare results with those obtained via other commonly used techniques such as zero-phonon line fitting and non-negative matrix factorization. We find that DEP can be employed across different samples and experimental setups and yields consistent and quantitative results for NV charge state ratios that are in agreement with our understanding of NV photophysics. By providing robust NV charge state quantification across sample types and measurement platforms, DEP will support the development of NV-based quantum technologies.

Color center laser spectroscopy↗
DOE OSTI2026

Characterization of Precipitation-Induced Radon Progeny Deposition Events Using a City-Scale Sensor Network

Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. Here, we present a quantitative and qualitative description of rain-induced radon progeny deposition events occurring in Chicago from September 2023 to February 2024. We measure ambient gamma radiation levels, precipitation rate, temperature, pressure, and relative humidity in a network of sensor nodes. For each identified precipitation period, we decompose spectra into static- and radon-associated components as defined by a non-negative matrix factorization (NMF) algorithm. We find a consistent power-law relationship between a precipitation-dependent peak of the radon progeny proxy (RPP) and the peak strength of the radon-associated NMF component for most precipitation events. We conduct a case study of a rainfall period with abnormally high levels of implied radon progeny concentration and describe its temporal and spatial evolution. We hypothesize that this phenomenon is due to the air mass path that intersects a uranium-rich region of Wyoming. Finally, we cluster precipitation events into three distinct categories. One category roughly corresponds to events with deep low-pressure systems and high relative radon concentration, while another is characteristic of light stratiform rain with slightly higher temperatures and intermediate relative radon concentration. The third category appears to contain weak-gradient or lake breeze convection showers with intermittent precipitation and low relative radon concentration. These findings suggest that radiological anomaly detection could be improved by training unique background models corresponding to each category of meteorological event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Data-Driven Optimization of Pixelated CdZnTe Spectrometers for Uranium Enrichment Assay

Here, in recent work [Vavrek et al. (2025)], we developed the performance optimization framework spectre-ml for gamma spectrometers with variable performance across many readout channels. The framework uses non-negative matrix factorization (NMF) and clustering to learn groups of similarly-performing channels and sweep through various learned channel combinations to optimize the performance tradeoff of including worse-performing channels for better total efficiency. In this work, we integrate the pyGEM uranium enrichment assay code with our spectre-ml framework, and show that the U-235 enrichment relative uncertainty can be directly used as an optimization target. We find that this optimization reduces relative uncertainties after a 30 -minute measurement by an average of 20%, as tested on six different H3D M400 CdZnTe spectrometers, which can significantly improve uranium non-destructive assay measurement times in nuclear safeguards contexts. Additionally, this work demonstrates that the spect re-ml optimization framework can accommodate arbitrary end-user spectroscopic analysis code and performance metrics, enabling future optimizations for complex Pu spectra.

Gamma-ray detection↗
DOE OSTI2026

Data-Driven Performance Optimization of Gamma Spectrometers With Many Channels

In gamma spectrometers with variable spectroscopic performance across many channels (e.g., many pixels or voxels), a tradeoff exists between including data from successively worse-performing readout channels and increasing efficiency. Brute-force calculation of the optimal set of included channels is exponentially infeasible as the number of channels grows, and approximate methods are required. In this work, we present a data-driven framework for attempting to find near-optimal sets of included detector channels. The framework leverages non-negative matrix factorization (NMF) to learn the behavior of gamma spectra across the detector and clusters similarly-performing detector channels together. Performance comparisons are then made between spectra with channel clusters removed, which is more feasible than brute force. The framework is general and can be applied to arbitrary, user-defined performance metrics depending on the application. We apply this framework to optimizing gamma spectra measured by H3D M400 CdZnTe (CZT) spectrometers, which exhibit variable performance across their crystal volumes. In particular, we show several examples optimizing various performance metrics for uranium and plutonium gamma spectra in non-destructive assay (NDA) for nuclear safeguards, and explore trends in performance versus parameters such as clustering algorithm type. We also compare the NMF + clustering pipeline to several non-machine-learning (ML) algorithms, including several greedy algorithms. Although, we find that the NMF + clustering pipeline tends to find the best-performing set of detector voxels, significantly improving over the unoptimized spectra, but that a greedy accumulation of spectra segmented by detector depth can, in some cases, give similar performance improvements in much less computation time.

Energy resolution↗
DOE OSTI2025

LaFA

Latent Feature Attacks on Non-negative Matrix Factorization

Bhattarai, Manish↗
NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗
DOE OSTI2025

Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island

Background: Urban heat island (UHI), where built areas are warmer compared to non-urban regions, increases human related diseases and mortality. A key challenge in UHI analysis is the designation of sites as urban or suburban/rural; however, the growing complexity of green spaces in urban areas and the predominance of the transportation sector in nonurban areas creates a dilemma for distinct delineation. Objectives: This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. Methods: Atmospheric gases and aerosols were used as direct tracers of urbanization for UHI analysis. Inorganic gases and particulate matter were monitored in two sites in a southeastern US city with varying degrees of urbanization. VOCs were analyzed using a proton transfer reaction time-of-flight mass spectrometer. Results: The more-urbanized site exhibited warmer night conditions and elevated total oxidant levels, leading to the formation of nanometer-sized particles. Machine learning analysis revealed similar atmospheric pollutant profiles for both sites, suggesting comparable sources and variability. Biogenic VOCs were enhanced at the less-urbanized site; however, levels of anthropogenic aromatic VOCs were comparable for both sites. A comprehensive mass spectra analysis revealed distinct molecular backbones per site that further affirmed the applicability of VOCs as indicators of urbanization. Conclusion: This study concludes that VOCs provide more direct and accurate information than typical inorganic gases and PM parameters for characterizing the degree of urbanization. Further exploration of VOCs can enhance our understanding of UHI dynamics and its interaction with vegetation in urban green spaces.

Air quality sensor↗