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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

CHANDRA IDENTIFICATION OF 26 NEW BLACK HOLE CANDIDATES IN THE CENTRAL REGION OF M31

We have previously identified 10 M31 black hole candidates (BHCs) in M31 from their X-ray properties alone. They exhibit ''hard state'' emission spectra that are seen at luminosities {approx}<10% Eddington in X-ray binaries (XBs) containing a neutron star (NS) or black hole, at luminosities that significantly exceed the NS threshold. Nine of these are associated with globular clusters (GCs); hence, these are most likely low mass X-ray binaries; eight are included in this survey. We have recently discovered that analysis of the long term 0.5-4.5 keV variability of XBs via structure functions allows us to separate XBs from active galactic nuclei, even though the emission spectra are often similar; this has enabled us to search for BHCs outside of GCs. We have identified 26 new BHCs (12 strong, 14 plausible) within 20' of the M31 nucleus (M31*), using 152 Chandra observations spaced over {approx}13 yr; some of our classifications were enhanced with XMM-Newton observations. Of these, seven appear within 100'' of M31*; this supports the theory suggesting that this region experiences enhanced XB production via dynamical processes similar to those seen in GCs. We have found a parameter space where our BHCs are separated from Galactic NS binaries: we show that modeling a simulated hard state spectrum with a disk blackbody + blackbody model yields parameters that lie outside the space occupied by NS binaries that are modeled this way. The probability that our BHCs all lie within the NS parameter space is {approx}3 Multiplication-Sign 10{sup -29}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

58 GEOSCIENCES↗
DOE OSTI2022

Perfecting Our Set of Spectrophotometric Standard DA White Dwarfs

We verified for photometric stability a set of DA white dwarfs with Hubble Space Telescope magnitudes from the near-ultraviolet to the near-infrared and ground-based spectroscopy by using time-spaced observations from the Las Cumbres Observatory network of telescopes. The initial list of 38 stars was whittled to 32 final ones, which comprise a high-quality set of spectrophotometric standards. These stars are homogeneously distributed around the sky and are all fainter than r ~ 16.5 mag. Their distribution is such that at least two of them would be available to be observed from any observatory on the ground at any time at airmass less than 2. Light curves and different variability indices from the Las Cumbres Observatory data were used to determine the stability of the candidate standards. When available, Pan-STARRS1, Zwicky Transient Facility, and TESS data were also used to confirm the star classification. Our analysis showed that four DA white dwarfs may exhibit evidence of photometric variability, while a fifth is cooler than our established lower temperature limit, and a sixth star might be a binary. In some instances, due to the presence of faint nearby red sources, care should be used when observing a few of the spectrophotometric standards with ground-based telescopes. Light curves and finding charts for all the stars are provided.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

RESOLVED NEAR-INFRARED SPECTROSCOPY OF WISE J104915.57-531906.1AB: A FLUX-REVERSAL BINARY AT THE L DWARF/T DWARF TRANSITION

We report resolved near-infrared spectroscopy and photometry of the recently identified brown dwarf binary WISE J104915.57-531906.1AB, located 2.02 {+-} 0.15 pc from the Sun. Low-resolution spectral data from Magellan/FIRE and IRTF/SpeX reveal strong H{sub 2}O and CO absorption features in the spectra of both components, while the secondary also exhibits weak CH{sub 4} absorption at 1.6 {mu}m and 2.2 {mu}m. Spectral indices and comparison to low-resolution spectral standards indicate component types of L7.5 and T0.5 {+-} 1, the former consistent with the optical classification of the primary. Both sources also have unusually red spectral energy distributions for their spectral types, which we attribute to enhanced condensate opacity (thick clouds). Relative photometry reveals a flux reversal between the J and K bands, with the T dwarf component being brighter in the 0.95-1.3 {mu}m region ({Delta}J = -0.31 {+-} 0.05). As with other L/T transition binaries, this reversal likely reflects the depletion of condensate opacity in the T dwarf, with the contrast enhanced by the thick clouds present in the photosphere of the L dwarf primary. The 1 {mu}m flux from the T dwarf most likely emerges from gaps in its cloud layer, as suggested by the significant optical variability detected from this source by Gillon et al. Component mass measurements of the WISE J1049-5319AB system through astrometric and component radial velocity monitoring may resolve the current debate as to whether the loss of photospheric condensate clouds at the L dwarf/T dwarf boundary is a slow or rapid process, a conceivable endeavor given its proximity, brightness, small separation (3.1 {+-} 0.3 AU), and reasonable orbital period (20-30 yr)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A SEARCH FOR L/T TRANSITION DWARFS WITH PAN-STARRS1 AND WISE. II. L/T TRANSITION ATMOSPHERES AND YOUNG DISCOVERIES

The evolution of brown dwarfs from L to T spectral types is one of the least understood aspects of the ultracool population, partly for lack of a large, well-defined, and well-characterized sample in the L/T transition. To improve the existing census, we have searched ≈28,000 deg{sup 2} using the Pan-STARRS1 and Wide-field Infrared Survey Explorer surveys for L/T transition dwarfs within 25 pc. We present 130 ultracool dwarf discoveries with estimated distances ≈9–130 pc, including 21 that were independently discovered by other authors and 3 that were previously identified as photometric candidates. Seventy-nine of our objects have near-IR spectral types of L6–T4.5, the most L/T transition dwarfs from any search to date, and we have increased the census of L9–T1.5 objects within 25 pc by over 50%. The color distribution of our discoveries provides further evidence for the “L/T gap,” a deficit of objects with (J − K){sub MKO} ≈ 0.0–0.5 mag in the L/T transition, and thus reinforces the idea that the transition from cloudy to clear photospheres occurs rapidly. Among our discoveries are 31 candidate binaries based on their low-resolution spectral features. Two of these candidates are common proper motion companions to nearby main sequence stars; if confirmed as binaries, these would be rare benchmark systems with the potential to stringently test ultracool evolutionary models. Our search also serendipitously identified 23 late-M and L dwarfs with spectroscopic signs of low gravity implying youth, including 10 with vl-g or int-g gravity classifications and another 13 with indications of low gravity whose spectral types or modest spectral signal-to-noise ratio do not allow us to assign formal classifications. Finally, we identify 10 candidate members of nearby young moving groups (YMG) with spectral types L7–T4.5, including three showing spectroscopic signs of low gravity. If confirmed, any of these would be among the coolest known YMG members and would help to determine the effective temperature at which young brown dwarfs cross the L/T transition.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

LIGHT CURVES OF 32 LARGE TRANSNEPTUNIAN OBJECTS

We present observations of 32 primarily bright, newly discovered Transneptunian objects (TNOs) observable from the Southern Hemisphere during 39 nights of observation with the Irenee du Pont 2.5 m telescope at Las Campanas Observatory. Our dataset includes objects in all dynamical classes, but is weighted toward scattered objects. We find 15 objects for which we can fit periods and amplitudes to the data, and place light curve amplitude upper limits on the other 17 objects. Combining our sample with the larger light curve sample in the literature, we find a 3{sigma} correlation between light curve amplitude and absolute magnitude with fainter objects having larger light curve amplitudes. We looked for correlations between light curve and individual orbital properties, but did not find any statistically significant results. However, if we consider light curve properties with respect to object dynamical classification, we find statistically different distributions between the classical-scattered and classical-resonant populations at the 95.60% and 94.64% level, respectively, with the classical objects having larger amplitude light curves. The significance is 97.05% if the scattered and resonant populations are combined. The properties of binary light curves are largely consistent with the greater TNO population except in the case of tidally locked systems. All the Haumea family objects measured so far have light curve amplitudes and rotation periods {<=}10 hr, suggesting that they are not significantly different from the larger TNO population. We expect multiple factors are influencing object rotations: object size dominates light curve properties except in the case of tidal, or proportionally large collisional interactions with other TNOs, the influence of the latter being different for each TNO sub-population. We also present phase curves and colors for some of our objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CEGANN: CRYSTAL EDGE GRAPH ATTENTION NEURAL NETWORK

SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.

CHAN, HENRYT↗
DOE OSTI2018

Automatic optical detection and classification of marine animals around MHK converters using machine vision

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Flexible and scalable methods for quantifying stochastic variability in the era of massive time-domain astronomical data sets

We present the use of continuous-time autoregressive moving average (CARMA) models as a method for estimating the variability features of a light curve, and in particular its power spectral density (PSD). CARMA models fully account for irregular sampling and measurement errors, making them valuable for quantifying variability, forecasting and interpolating light curves, and variability-based classification. We show that the PSD of a CARMA model can be expressed as a sum of Lorentzian functions, which makes them extremely flexible and able to model a broad range of PSDs. We present the likelihood function for light curves sampled from CARMA processes, placing them on a statistically rigorous foundation, and we present a Bayesian method to infer the probability distribution of the PSD given the measured light curve. Because calculation of the likelihood function scales linearly with the number of data points, CARMA modeling scales to current and future massive time-domain data sets. We conclude by applying our CARMA modeling approach to light curves for an X-ray binary, two active galactic nuclei, a long-period variable star, and an RR Lyrae star in order to illustrate their use, applicability, and interpretation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Software classification using phylogenetic techniques

Software, such as malware, may be classified using phylogenetic techniques. An evolutionary history of a representative set of software programs may be reconstructed to generate a reference phylogeny. Dynamic traces of the representative software programs may be obtained. The dynamic traces may include time-ordered sequences of execution commands extracted from running software binaries. Metrics may be developed using the dynamic traces. One or more unknown software programs may then be classified against the reference phylogeny using the metrics developed using the dynamic traces of the representative set of software programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42,650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. Further, a classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Ultracool dwarfs candidates based on 6 yr of the Dark Energy Survey data

ABSTRACT We present a sample of 19 583 ultracool dwarf candidates brighter than z ≤23 selected from the Dark Energy Survey DR2 coadd data matched to VHS DR6, VIKING DR5, and AllWISE covering ∼ 480 deg2. The ultracool candidates were first pre-selected based on their (i–z), (z–Y), and (Y–J) colours. They were further classified using a method that compares their optical, near-infrared, and mid-infrared colours against templates of M, L, and T dwarfs. 14 099 objects are presented as new L and T candidates and the remaining objects are from the literature, including 5342 candidates from our previous work. Using this new and deeper sample of ultracool dwarf candidates we also present: 20 new candidate members to nearby young moving groups and associations, variable candidate sources and four new wide binary systems composed of two ultracool dwarfs. Finally, we also show the spectra of 12 new ultracool dwarfs discovered by our group and presented here for the first time. These spectroscopically confirmed objects are a sanity check of our selection of ultracool dwarfs and photometric classification method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗
DOE OSTI2015

Threshold selection for classification of MR brain images by clustering method

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗
DOE OSTI2019

Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

We report that machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the case of studies that rely on state-of-the-art computational models which typically take days to run, thus hindering the potential of machine learning tools. In this work, we present a novel classifier that takes advantage of lower fidelity models and inexpensive approximations to predict the binary output of expensive computer simulations. We postulate an autoregressive model between the different levels of fidelity with Gaussian process priors. We adopt a fully Bayesian treatment for the hyper-parameters and use Markov Chain Monte Carlo samplers. We take advantage of the probabilistic nature of the classifier to implement active learning strategies. We also introduce a sparse approximation to enhance the ability of the multi-fidelity classifier to handle a large amount of low fidelity samples. We test these multi-fidelity classifiers against their single-fidelity counterpart with synthetic data, showing a median computational cost reduction of 23% for a target accuracy of 90%. In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99.6% compared to 74.1% of a single-fidelity classifier when both are trained with 50 samples. In general, our results show that the multi-fidelity classifiers outperform their single-fidelity counterpart in terms of accuracy in all cases. Finally, we envision that this new tool will enable researchers to study classification problems that would otherwise be prohibitively expensive. Source code is available at https://github.com/fsahli/MFclass.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗