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DOE OSTI2023

Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1981

Spectral behavior of wheat yield variety trials

Little variation between varieties is seen at jointing, but the variability is found to increase during grain filling and decline again at maturity. No relationship is found between spectral response and yield, and when yields are segregated into various classes the spectral response is the same. Spring and winter nurseries are found to separate during the reproductive stage because of differences in dates of heading and maturity, but they exhibit similar spectral responses. The transformed normalized difference is at a minimum after the maximum grain weight occurs and the leaves begin to brown and fall off. These data of 100% ground cover demonstrate that it is not possible to predict grain yield from only spectral data. This, however, may not apply when reduced yields are caused by less-than-full ground cover

Hatfield, J. L.↗

DOE OSTI2021

Laboratory Data on the EUV/Soft-X-Ray Flux Problem of Fe XVIII and Fe XIX

Observations by the spacecraft Extreme Ultraviolet Explorer (EUVE), Far Ultraviolet Spectroscopic Explorer (FUSE), Chandra, and XMM-Newton of Capella (α Aurigae) have encountered problems with the relative intensities of the Fe XVIII and Fe XIX line emission in the soft-X-ray (XUV) and extreme-ultraviolet spectral ranges versus various model predictions based on theoretical atomic data. The reason may either lie in astrophysical phenomena, e.g., at emission or because of absorption by the interstellar medium, in the theoretical atomic data, in one or more spectrometer calibrations, or in the spectral modeling. Here, by measurements using an electron beam ion trap we provide laboratory data obtained under conditions that are reasonably close to stellar emission regions. The laboratory data on Fe XVIII and Fe XIX show line ratios that are rather similar to the observations of Capella. The measurements, therefore, rule out astrophysical phenomena and calibration errors and point to issues with the modeling of the observed Capella emission.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1995

A spatially adaptive spectral re-ordering technique for lossless coding of hyper-spectral images

In this paper, we propose a new approach, applicable to lossless compression of hyper-spectral images, that alleviates some limitations of linear prediction as applied to this problem. According to this approach, an adaptive re-ordering of the spectral components of each pixel is performed prior to prediction and encoding. This re-ordering adaptively exploits, on a pixel-by pixel basis, the presence of inter-band correlations for prediction. Furthermore, the proposed approach takes advantage of spatial correlations, and does not introduce any coding overhead to transmit the order of the spectral bands. This is accomplished by using the assumption that two spatially adjacent pixels are expected to have similar spectral relationships. We thus have a simple technique to exploit spectral and spatial correlations in hyper-spectral data sets, leading to compression performance improvements as compared to our previously reported techniques for lossless compression. We also look at some simple error modeling techniques for further exploiting any structure that remains in the prediction residuals prior to entropy coding.

Memon, Nasir D.↗

NASA NTRS1992

Mapping vegetation types with the multiple spectral feature mapping algorithm in both emission and absorption

Vegetation covers a large portion of the Earth's land surface. Remotely sensing quantitative information from vegetation has proven difficult because in a broad sense, all vegetation is similar from a chemical viewpoint, and most healthy plants are green. Plant species are generally characterized by the leaf and flower or fruit morphology, not by remote sensing spectral signatures. But to the human eye, many plants show varying shades of green, so there is direct evidence for spectral differences between plant types. Quantifying these changes in a predictable manner has not been easy. The Clark spectral features mapping algorithm was applied to mapping spectral features in vegetation species.

Clark, Roger N.↗

DOE OSTI2022

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R 2 ) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

09 BIOMASS FUELS↗

NASA NTRS1982

Ultraviolet photometry of A-type stars at high galactic latitudes

Ultraviolet photometry on the International Ultraviolet Explorer (IUE) was used to study four stars located in one target of an earlier diffuse background sounding-rocket experiment. The resulting stellar correction is much smaller than that previously estimated, giving a higher diffuse background at this target. Visible photometry appears to be a better indicator of ultraviolet flux than spectral type. The discrepancy between previous predictions and the present observations is explained in terms of: (1) misclassification of two stars; (2) use of a spectral type/effective temperature calibration hotter than more recent determinations; and (3) inadequacy of the Kurucz models, in the far ultraviolet, for A-type stars.

Landsman, W. B.↗

NASA NTRS2007

Physical Interpretation of the Correlation Between Multi-Angle Spectral Data and Canopy Height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Schull, M. A.↗

DOE OSTI2025

Interacting Dirac magnons in the van der Waals ferromagnet CrBr 3

We study the effects of magnon-magnon interactions in the two-dimensional van der Waals ferromagnet CrBr 3 focusing on its honeycomb lattice structure. Motivated by earlier theoretical predictions of temperature-induced spectral shifts and van Hove singularities in the magnon dispersion [S. S. Pershoguba et al., Phys. Rev. X 8, 011010 (2018)], we go beyond the commonly used thermal magnon approximation by applying second-order perturbation theory in a fully numerical framework. Our analysis uncovers significant deviations from previous analysis: in particular, the predicted singularities are absent, consistent with recent inelastic neutron scattering measurements [S. E. Nikitin et al ., Phys. Rev. Lett. 129, 127201 (2022)]. Moreover, we find that the temperature dependence of the renormalized magnon spectrum exhibits a distinct 𝑇 3 behavior for the optical magnon branch, while retaining 𝑇 2 behavior for the acoustic or down magnon band. This feature sheds light on the collective dynamics of Dirac magnons and their interactions. We further compare the honeycomb case with a triangular Bravais lattice, relevant for ferromagnetic monolayer MnBi 2 ⁢Te 4 , and show that both systems lack singular features while displaying quite distinct thermal trends.

Holstein-Primakoff method↗

NASA NTRS1980

Comparison of methods for prediction of transition by stability analysis

The paper compares several methods of transition prediction of linear stability analysis. The spectral stability analysis code SALLY is used to analyze flows over laminar flow control wings. It is shown that transition prediction by the envelope method and a new modified wave packet method are comparable in reliability, but that the envelope method is more efficient computationally. This is based on the results which show that the wave packet method provides N factors which are at best as consistent as those of the envelope method; since the wave packet method is at least 3 times as expensive as the envelope method, the latter is recommended for engineering design calculations.

Malik, M. R.↗

NASA NTRS2017

Predicting Electron Population Characteristics in 2-D Using Multispectral Ground-Based Imaging

Ground-based imaging and in situ sounding rocket data are compared to electron transport modeling for an active inverted-V type auroral event. The Ground-to-Rocket Electrodynamics-Electrons Correlative Experiment (GREECE) mission successfully launched from Poker Flat, Alaska, on 3 March 2014 at 11:09:50 UT and reached an apogee of approximately 335 km over the aurora. Multiple ground-based electron-multiplying charge-coupled device (EMCCD) imagers were positioned at Venetie, Alaska, and aimed toward magnetic zenith. The imagers observed the intensity of different auroral emission lines (427.8, 557.7, and 844.6 nm) at the magnetic foot point of the rocket payload. Emission line intensity data are correlated with electron characteristics measured by the GREECE onboard electron spectrometer. A modified version of the GLobal airglOW (GLOW) model is used to estimate precipitating electron characteristics based on optical emissions. GLOW predicted the electron population characteristics with 20% error given the observed spectral intensities within 10 deg of magnetic zenith. Predictions are within 30% of the actual values within 20 deg of magnetic zenith for inverted-V-type aurora. Therefore, it is argued that this technique can be used, at least in certain types of aurora, such as the inverted-V type presented here, to derive 2-D maps of electron characteristics. These can then be used to further derive 2-D maps of ionospheric parameters as a function of time, based solely on multispectral optical imaging data.

multispectral ground-based imaging↗

NASA NTRS2016

User's Manual: Routines for Radiative Heat Transfer and Thermometry

Determining the intensity and spectral distribution of radiation emanating from a heated surface has applications in many areas of science and engineering. Areas of research in which the quantification of spectral radiation is used routinely include thermal radiation heat transfer, infrared signature analysis, and radiation thermometry. In the analysis of radiation, it is helpful to be able to predict the radiative intensity and the spectral distribution of the emitted energy. Presented in this report is a set of routines written in Microsoft Visual Basic for Applications (VBA) (Microsoft Corporation, Redmond, Washington) and incorporating functions specific to Microsoft Excel (Microsoft Corporation, Redmond, Washington) that are useful for predicting the radiative behavior of heated surfaces. These routines include functions for calculating quantities of primary importance to engineers and scientists. In addition, the routines also provide the capability to use such information to determine surface temperatures from spectral intensities and for calculating the sensitivity of the surface temperature measurements to unknowns in the input parameters.

opticoelectronic devices↗

DOE OSTI2024

Modeling aerosol transmission spectra from n(λ) and k(λ) infrared optical constants measurements of organic liquids and solids

The effects of light scattering and refraction play significantly different roles for aerosols than for bulk materials, making it challenging to identify aerosolized chemicals using traditional spectral methods or spectral reference libraries. Due to a potentially infinite number of particle morphologies, sizes, and compositions, constructing a database of laboratory-measured aerosol spectra is not a practical solution. Here, as an alternative approach, the measured n / k optical vectors of two example organic materials (diethyl phthalate and D-mannitol) are used in combination with particle absorption / scattering theory (Mie theory and FDTD) and the Beer-Lambert law to generate a series of synthetic infrared transmission / scattered light spectra. The synthetic spectra show significant differences versus simple slab transmission spectra, even for small changes in particle size (e.g., 5 vs. 10 µm) for both single particles and ensembles, potentially serving as useful reference data for aerosol sensing. For spherical single particles with diameters of 1 to 10 µm, FDTD simulations predict changes in the magnitudes of spectral shifts and the shapes of the peaks vs. particle size with only small deviations from Mie theory predictions, yet reliably capture the direction of the shifts. Typical spectral peak shifts in the longwave infrared correspond to Δλ ∼0.20 µm (∼34 cm -1 ) when compared to corresponding slab transmission spectra. Additionally, synthetic spectra generated from the n / k values derived using two different methods (KBr pellet transmission and single-angle reflectance) are compared using the Mie theory model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

The convergence of theory and experiment in direct combustion generated noise

Current theories of combustion generated noise are reviewed with regard to their ability to predict the sound power output and spectral characteristics of noise generated by several flame types. New experimental information on open turbulent flames and on gas turbine combustor cans is presented. Available information on gas phase diffusion flames is reviewed. It is concluded that if some of the gross turbulence features of the flame are known and if the acoustical behavior of any flame enclosure is known, then scaling rules for behavior of the sound power output and spectral content may be quite accurately produced by theory. On the other hand, the theory is not sufficiently advanced to make absolute predictions; such predictions must await more detailed knowledge of turbulent flame structure.

Strahle, W. C.↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

NASA NTRS1989

A modified VAPEPS method for predicting vibroacoustic response of unreinforced mass loaded honeycomb panels

VAPEPS (VibroAcoustic Payload Environment Prediction System) is a computer program used to predict the vibroacoustic response of a structure. An alternate VAPEPS modeling technique, the Modified NASA Lewis Method, is an improvement for modeling unreinforced mass loaded honeycomb panels. The Modified NASA Lewis Method prediction is compared to the standard ASMS VAPEPS prediction, and the acoustic test data for three spacecraft panels. An analytical method of computing variance is presented and used to compute 95 percent confidence levels. These levels are compared to the standard VAPEPS confidence levels and to the envelope of the test data. As a result of using the new methodology suggested in the paper, both the mean prediction and the 95 percent confidence level prediction agree well with the test data in both spectral shape and magnitude. Therefore, the Modified NASA Lewis Method prediction methodology may be used to define more realistic random vibration test levels.

Mcnelis, Mark E.↗

NASA NTRS1989

A modified VAPEPS method for predicting vibroacoustic response of unreinforced mass loaded honeycomb panels

VAPEPS (vibroAcoustic Payload Environment Prediction System) is a computer program used to predict the vibroacoustic response of a structure. An alternate VAPEPS modeling technique, the Modified NASA Lewis Method, is an improvement for modeling unreinforced mass loaded honeycomb panels. The Modified NASA Lewis Method prediction is compared to the standard ASMS VAPEPS prediction, and the acoustic test data for three spacecraft panels. An analytical method of computing variance is presented and used to compute 95 percent confidence levels. These levels are compared to the standard VAPEPS confidence levels and to the envelope of the test data. As a result of using the new methodology suggested in the paper, both the mean prediction and the 95 percent confidence level prediction agree well with the test data in both spectral shape and magnitude. Therefore, the Modified NASA Lewis Method prediction methodology may be used to define more realistic random vibration test levels.

Mcnelis, Mark E.↗

DOE OSTI2024

Local Markers for Crystalline Topology

Over the last few years, crystalline topology has been used in photonic crystals to realize edge- and corner-localized states that enhance light-matter interactions for potential device applications. However, the band-theoretic approaches currently used to classify bulk topological crystalline phases cannot predict the existence, localization, or spectral isolation of any resulting boundary-localized modes. While interfaces between materials in different crystalline phases must have topological states at some energy, these states need not appear within the band gap, and thus may not be useful for applications. Here, we derive a class of local markers for identifying material topology due to crystalline symmetries, as well as a corresponding measure of topological protection. As our real-space-based approach is inherently local, it immediately reveals the existence and robustness of topological boundary-localized states, yielding a predictive framework for designing topological crystalline heterostructures. In conclusion, beyond enabling the optimization of device geometries, we anticipate that our framework will also provide a route forward to deriving local markers for other classes of topology that are reliant upon spatial symmetries.

74 ATOMIC AND MOLECULAR PHYSICS↗