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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 OSTI2024

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Bjorner, Marianne B.↗
DOE OSTI2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Can tonne-scale direct detection experiments discover nuclear dark matter?

Models of nuclear dark matter propose that the dark sector contains large composite states consisting of dark nucleons in analogy to Standard Model nuclei. We examine the direct detection phenomenology of a particular class of nuclear dark matter model at the current generation of tonne-scale liquid noble experiments, in particular DEAP-3600 and XENON1T. In our chosen nuclear dark matter scenario distinctive features arise in the recoil energy spectra due to the non-point-like nature of the composite dark matter state. We calculate the number of events required to distinguish these spectra from those of a standard point-like WIMP state with a decaying exponential recoil spectrum. In the most favourable regions of nuclear dark matter parameter space, we find that a few tens of events are needed to distinguish nuclear dark matter from WIMPs at the 3 σ level in a single experiment. Given the total exposure time of DEAP-3600 and XENON1T we find that at best a 2 σ distinction is possible by these experiments individually, while 3 σ sensitivity is reached for a range of parameters by the combination of the two experiments. We show that future upgrades of these experiments have potential to distinguish a large range of nuclear dark matter models from that of a WIMP at greater than 3 σ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Toward large-scale building detection with convolutional neural network using remote sensing images

Establishing up-to-date nationwide building maps is essential to understand urban dynamics, such as estimating population and urban planning and many other applications. However, an efficient and effective solution is yet to be developed. In this paper, for the first time we evaluate three state-of-the-art CNNs for detecting buildings across entire United States using aerial images. The three CNN architectures, fully convolutional neural network, conditional random field as recurrent neural network, and SegNet, support semantic pixel-wise labeling and focus on capturing textural information at multi-scale. We use 1-meter resolution NAIP images as the test data set, and compare the detection results across the three methods. In addition, we propose to combine signed distance function labels with SegNet, which is the preferred CNN architecture identified by our extensive evaluations. The results are further improved in terms of precision, recall rate and the number of building detected. On average, model inference on test images is less than one minute for an area of size $\sim56$ $km^2$. With these promising results and the time required to process images, the framework offers great potential toward country scale building mapping with remote sensing imagery.

Yang, Lexie↗
DOE OSTI2021

Detecting multi‐scale riverine topographic variability and its influence on Chinook salmon habitat selection

Abstract Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

Duffin, Jenna↗
DOE OSTI2022

Towards scaling community detection on distributed-memory heterogeneous systems

Distributed multi-GPU systems pose significant challenges and opportunities for efficient execution of parallel applications. Graph algorithms are generally characterized by irregular memory accesses, low computation to communication ratios, and load balancing problems that are especially hard to address on multi-GPU systems. Graph community detection is an important problem in the emerging domain of graph analytics with numerous applications. In this paper, we present our ongoing work on distributed-memory multi-GPU implementation for graph community detection. Our work parallelizes the widely used (albeit serial) Louvain method on distributed multi-GPU platforms. Supported by an extensive set of experiments on a multi-GPU enabled supercomputer (OLCF Summit) and a single compute node (Nvidia DGX-2®), we demonstrate competitive performance to existing distributed-memory CPU-based implementation, and up to 6.5 better results than Nvidia RAPIDS® CUGRAPH. To the best of our knowledge, this work represents the first effort for community detection on distributed multi-GPU systems. Our approach and related findings can be extended to numerous other iterative graph algorithms on multi-GPU systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs." We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times 6$ km 2 area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. Here, we show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this article, we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Delaunay density diagnostic

The Delaunay density diagnostic helps detect and assess geometric scales within unstructured numerical data sets. The algorithm was first introduced in the paper "Data-driven geometric scale detection via Delaunay interpolation" by Andrew Gillette and Eugene Kur, 2022. More details can be found in that paper, as well as the included README file.

Gillette, AndrewK↗
DOE OSTI2024

Exploring the Potential of Residual Impurities in Germanium Detectors for MeV-Scale Dark Matter Detection

Abstract The direct detection of MeV-scale dark matter (DM) particles hinges on achieving an exceptionally low-energy detection threshold. Germanium (Ge) detectors, meticulously tailored with precise impurity compositions, hold the potential to enhance sensitivity to energy levels below the sub-electronvolt (sub-eV) range. This study explores the behavior of residual impurities inherent to Ge detectors at helium temperatures, unveiling a captivating freeze-out phenomenon leading to the formation of excited localized states known as dipole states. Using compelling evidence from relative capacitance measurements obtained from two detectors, we elucidate the transition of impurity atoms from free charge states to these dipole states as the temperature drops from 11 to 6.5 K. Our investigation comprehensively covers the intricate formation of these dipole states in bothn-type andp-type impurities. Furthermore, we shed light on the electric field generated by these dipole states, revealing their ability to trap charges and facilitate the creation of cluster dipole states. Confirming findings from previous measurements, we establish that these excited dipole states exhibit a binding energy of less than 10 meV, offering an exceptionally low detection threshold for MeV-scale DM. Building upon this concept, we propose the development of a 1-kg Ge detector with internal charge amplification—an innovative approach poised to surpass electrical noise and enable the detection of MeV-scale DM with unprecedented sensitivity.

Physics↗
DOE OSTI2014

Boundary between the thermal and statistical polarization regimes in a nuclear spin ensemble

As the number of spins in an ensemble is reduced, the statistical fluctuations in its polarization eventually exceed the mean thermal polarization. This transition has now been surpassed in a number of recent nuclear magnetic resonance experiments, which achieve nanometer-scale detection volumes. Here, we measure nanometer-scale ensembles of nuclear spins in a KPF{sub 6} sample using magnetic resonance force microscopy. In particular, we investigate the transition between regimes dominated by thermal and statistical nuclear polarization. The ratio between the two types of polarization provides a measure of the number of spins in the detected ensemble.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Network Anomaly Detection in Distributed Edge Computing Infrastructure

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Marfo, William [University of Texas at El Paso,Dep↗
DOE OSTI2020

Proton beam position measurement in air using a BPM

A Beam Position Monitor (BPM) is potentially useful to measure the position and phase of the beam in air in a non-destructive way. An air-gap BPM in experiments, such as beam-induced radioactive waste management and dynamic radiography applications, where a so-called air gap is needed, can be utilized to measure the beam position and phase. In this study, a stripline BPM was used in the air-gap of an 800 MeV proton beam transport line. The downstream end of the primary beamline exit window was made of a thin aluminum plate and allowed the beam to travel 1.2 m in ambient air before re-entering into a vacuum drift section. Such a configuration was arranged to examine the BPM effectiveness in atmospheric temperature and pressure where ionization of air occurs. In this study, a high energy (800 MeV), high current (0.6 A beam peak current/pulse) proton beam of 5 mm radius was transported in the air. The beam position relative to the axis was measured by detecting the signature of the beam in a nanosecond scale. This nanosecond scale detection ability was useful to identify other signals such as plasma effects. The BPM signals were processed at a frequency of 201 MHz; thus, one gets a stronger response in a stripline pattern as it was used in this study instead of a dot-type BPM. Experimental data show that the BPM works well in air, but ionization of air or plasma formation could not be measured over the BPM signal. The design, construction, and performance of a BPM in air environment are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Two-dimensional nuclear magnetic resonance spectroscopy with a microfluidic diamond quantum sensor

Quantum sensors based on nitrogen-vacancy centers in diamond have emerged as a promising detection modality for nuclear magnetic resonance (NMR) spectroscopy owing to their micrometer-scale detection volume and noninductive-based detection. A remaining challenge is to realize sufficiently high spectral resolution and concentration sensitivity for multidimensional NMR analysis of picoliter sample volumes. Here, we address this challenge by spatially separating the polarization and detection phases of the experiment in a microfluidic platform. We realize a spectral resolution of 0.65 ± 0.05 Hz, an order-of-magnitude improvement over previous diamond NMR studies. We use the platform to perform two-dimensional correlation spectroscopy of liquid analytes within an effective ~40-picoliter detection volume. The use of diamond quantum sensors as in-line microfluidic NMR detectors is a major step toward applications in mass-limited chemical analysis and single-cell biology.

Science & Technology - Other Topics↗
DOE OSTI2015

Anomaly Detection In Additively Manufactured Parts Using Laser Doppler Vibrometery

Additively manufactured parts are susceptible to non-uniform structure caused by the unique manufacturing process. This can lead to structural weakness or catastrophic failure. Using laser Doppler vibrometry and frequency response analysis, non-contact detection of anomalies in additively manufactured parts may be possible. Preliminary tests show promise for small scale detection, but more future work is necessary.

42 ENGINEERING↗
DOE OSTI2023

Aqueous phosphate detection using Eu(acac) 3

Currently, the majority of phosphate-sensitive phosphors reported in literature are metal-based complexes that require complicated and expensive synthesis to prepare. Here, in this study, we investigate the phosphate detecting capabilities of an inexpensive off-the-shelf phosphor: Eu(acac) 3 . We characterize its phosphate sensitivity as a function of sensor concentration and find that the quenching coefficient is inversely proportional to sensor concentration. This results in the limit-of-detection scaling linearly with sensor concentration. We determine its concentration-independent sensitivity to be 1.558 ± 0.012, which results in our lowest sensor concentration (44 μM) having a limit-of-detection of 3.39 ± 0.68 μM. We also find that for phosphate concentrations less than the sensor concentration, the fluorescence intensity ratio behaves linearly, but transitions to a non-linear functionality as the phosphate concentration exceeds the sensor concentration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs but attempts to combine different algorithms and optimize them on large datasets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in identification of 540 SAAVs in the mass spectrometry data, the most comprehensive coverage yet reported. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53 and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

59 BASIC BIOLOGICAL SCIENCES↗