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

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Solving challenges in QCD 3D partonic distributions

The superior luminosity and high polarization of CEBAF, combined with the high resolution and excellent particle identification capabilities of its detectors, as well as the ability for multidimensional and multiparticle detection using polarized targets, make Jefferson Lab uniquely positioned to disentangle the genuine intrinsic transverse structure of hadrons encoded in 3D partonic distributions—particularly in the kinematic regime dominated by valence quarks. The inclusion of Jefferson Lab data on semi-inclusive and hard exclusive hadron production has the potential to dramatically advance our understanding of non-perturbative QCD dynamics. Although a wealth of data from various Jefferson Lab experiments is already available, its incorporation into phenomenological studies has been slow, and a significant portion of data from other leptoproduction experiments is still missing from global fits. In this contribution, we discuss the existing challenges and outline a path forward for improving the analysis of low center-of-mass electroproduction experiments in general, and Jefferson Lab data in particular.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Simple and Effective Squash-PCR for Rapid Genotyping of Industrial Microalgae

Microalgae are recognized for their versatility in providing renewable energy, biopharmaceuticals, and nutraceuticals, attributed to their sustainable, renewable, and cost-effective nature. Genetic engineering has proven highly effective in enhancing microalgae production. PCR-based genotyping is the primary method for screening genetically transformed microalgae cells. Recently, we developed a novel PCR method, namely Squash-PCR, and employed it for the molecular analysis of industrially important fungi and yeasts. In this study, we successfully implemented the Squash-PCR technique in 12 industrially significant algae species. This approach offers a quick and reliable means of obtaining DNA templates directly from squashed algal cells, eliminating the need for time-consuming and labor-intensive cultivation and genomic DNA extraction steps. Our results demonstrate the effectiveness of Squash-PCR in detecting and characterizing target genes of interest in 12 different algae species. Overall, this study establishes the Squash-PCR method as a valuable tool for molecular studies in algae, enabling researchers to rapidly screen and manipulate genetic traits in diverse algal species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning

Many self-supervised methods have been proposed with the target of image anomaly detection. These methods often rely on the paradigm of data augmentation with predefined transformations such as flipping, cropping, and rotations. However, it is not straightforward to apply these techniques for non-image data, such as time series or tabular data, while the performance of the existing deep approaches has been under our expectation on tasks beyond images. In this work, we propose a novel active learning (AL) scheme that relied on neural autoregressive flows (NAF) for self-supervised anomaly detection, specifically on small-scale data. Unlike other generative models such as GANs or VAEs, flow-based models allow to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-AL method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The samples with lower likelihoods are selected and further checked by outlier detection using Mahalanobis distance. The augmented samples incorporating with normal samples are used for training a better detector so as to approach decision boundaries. Compared with random transformations, NAF-AL can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple time series and tabular datasets, and a real-world application in advanced manufacturing, with significant improvement on anomaly detection accuracy and robustness over the state-of-the-art.

Zhang, Jiaxin↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2026

New thermal-relic targets for sub-GeV dark matter direct detection

Dark matter direct detection experiments involving electron recoils are beginning to test highly predictive, thermal-relic milestones for sub-GeV dark matter models. Due to the Lee-Weinberg bound, thermal dark matter candidates in this mass range necessarily require comparably light mediator particles to achieve a suitably large annihilation cross section. Here we present new thermal-relic milestones for sub-GeV dark matter candidates that couple to vector mediators. In these models, the mediators are massive gauge bosons of anomaly-free Abelian extensions to the Standard Model, including the dark photon, gauged L i - L j , B - L , and B - 3 L i models, where B is the baryon number, L is the lepton number, and i , j index the lepton families. Since the same interactions that govern cosmological production also govern electron scattering, the targets we present are firmly predictive and allow for these models to be robustly discovered or falsified. Furthermore, since the mediators we study exhaust the minimal anomaly-free U(1) extensions to the Standard Model, our results offer a complete list of predictive milestones for sub-GeV dark matter coupled to vector mediators.

Han, Xu [Chicago U.] (ORCID:0009000821607836)↗
DOE OSTI2025

High-precision Measurement of the 16 O($n, n'γ$) Cross Section using $γ$-ray Detection in Liquid Scintillators with H 2 O and BeO Targets

The 16 O($n, n'γ$) reaction was measured at the Los Alamos Neutron Science Center white neutron source using γ-ray detection in liquid scintillators present in the upper hemisphere of the Correlated Gamma-Neutron Array for sCattering (CoGNAC). Separate measurements of this reaction were performed using H 2 O and BeO targets in successive years. The unique high energies of γ rays emitted from the 16 O($n, n'γ$) reaction facilitated a clean selection of this reaction from threshold to 9.8 MeV incident neutron energy without the need for precise measurements of the γ-ray energy or the scattered neutrons. The precise time resolution of the liquid scintillator detectors was then exploited to obtain high-resolution incident neutron energy measurements, and good agreement was obtained between the H 2 O and BeO results reported here. The dominant literature data sets for this reaction have systematic differences between them, but the present results improve upon the neutron energy resolution of earlier measurements and show important discrepancies in recent data. Finally, tentative data are also shown up to 20 MeV incident neutron energy but are potentially subject to improved understanding of the relative γ-ray and α decay branches from 16 O excited states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Simple, efficient and open-source CRISPR/Cas9 strategy for multi-site genome editing in Populus tremula × alba

Although the CRISPR/Cas9 system has been successfully used for crop breeding, its application remains limited in forest trees. Here, we describe an efficient gene editing strategy for hybrid poplar, (Populus tremula × alba INRA clone 717-1B4) based on the Golden Gate MoClo cloning. To test the system efficiency for generating single gene mutants, two single guide RNAs (sgRNAs) were designed and incorporated into the MoClo Tool Kit level 2 binary vector with the Cas9 expression cassette to mutate the SHORT ROOT (SHR) gene. Moreover, we also tested its efficiency for introducing mutations in two genes simultaneously by expressing one sgRNA targeting a single site of the YUC4 gene and the other sgRNA targeting the PLT1 gene. For a robust evaluation of the approach, we repeated the strategy to target the LBD12 and LBD4 genes simultaneously, using an independent construct. We generated hairy roots by Agrobacterium rhizogenes-mediated leaf transformation. Sequencing results confirmed the CRISPR/Cas9-mediated mutation in the targeted sites of PtaSHR. Biallelic and homozygous knockout mutations were detected. A deletion spanning both target sites and small insertions/deletions were the most common mutations. Out of the 22 SHR alleles sequenced, 21 were mutated. The phenotype’s characterization showed that transgenic roots with biallelic mutations for the SHR gene lacked a defined endodermal single cell layer, suggesting a conserved gene function similar to its homolog in Arabidopsis Arabidopsis thaliana (L.) Heynh. Sequencing results also revealed the high efficiency of the system for generating double mutants. Biallelic mutations for both genes in the yuc4/plt1 and lbd12/lbd4 roots were detected in three (yuc4/plt1) and two (lbd12/lbd4) out of four transgenic roots evaluated. A small deletion or a single nucleotide insertion at the single target site was the most common mutations. This CRISPR/Cas9 strategy arises as a rapid, simple and standardized gene-editing tool to evaluate the gene role in essential developmental programs such as radial cell differentiation of poplar roots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Athermal phonon collection efficiency in diamond crystals for low mass dark matter detection

Here, we explored the efficacy of lab-grown diamonds as potential target materials for the direct detection of sub-GeV dark matter (DM) using metallic magnetic calorimeters (MMCs). Diamond, with its excellent phononic properties and the low atomic mass of the constituent carbon, can play a crucial role in detecting low mass dark matter particles. The relatively long electron-hole pair lifetime inside the crystal may provide discrimination power between the DM-induced nuclear recoil events and the background-induced electron recoil events. Utilizing the fast response times of the MMCs and their unique geometric versatility, we deployed a novel methodology for quantifying phonon dynamics inside diamond crystals. We demonstrated that lab-grown diamond crystals fabricated via the chemical vapor deposition (CVD) technique can satisfy the stringent quality requirements for sub-GeV dark matter searches. The high-quality polycrystalline CVD diamond showed a superior athermal phonon collection efficiency compared to that of the reference sapphire crystal, and achieved energy resolution 62.7 eV at the 8.05 keV copper fluorescence line. With this energy resolution, we explored the low-energy range below 100 eV and confirmed the existence of so-called low-energy excess (LEE) reported by multiple cryogenic experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework

The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dual modality endocavity biopsy imaging system and method

A dual modality endocavity imaging and treatment system for detection of cancer and targeted biopsy and treatment procedures, comprising: a housing; a nuclear detector system housed within the housing and configured for detecting nuclear radiation imaging data; an ultrasound detector system housed within the housing for detecting ultrasound imaging data; a needle associated with the housing and adjustably positionable relative thereto; and a data processing module configured to receive the nuclear radiation imaging data and the ultrasound imaging data and to generate and output an image showing the relative position of the needle and an endocavity object of interest. The needle may include a distinct radiation signature to facilitate imaging thereof.

Lall, Terrence↗
DOE OSTI2021

Large Adaptive Optics Survey for Substellar Objects around Young, Nearby, Low-mass Stars with Robo-AO

We present results from the Large Adaptive optics Survey for Substellar Objects, where the goal is to directly image new substellar companions (<70 M {sub Jup}) at wide orbital separations (≳50 au) around young (≲300 Myr), nearby (<100 pc), low-mass (≈0.1–0.8 M{sub ⊙}) stars. We report on 427 young stars imaged in the visible (i') and near-infrared (J or H ) simultaneously with Robo-AO on the Kitt Peak 2.1 m telescope and later the Maunakea University of Hawaii 2.2 m telescope. To undertake the observations, we commissioned a new infrared camera for Robo-AO that uses a low-noise high-speed SAPHIRA avalanche photodiode detector. We detected 121 companion candidates around 111 stars, of which 62 companions are physically associated based on Gaia DR2 parallaxes and proper motions, another 45 require follow-up observations to confirm physical association, and 14 are background objects. The companion separations range from 2 to 1101 au and reach contrast ratios of 7.7 mag in the near-infrared compared to the primary. The majority of confirmed and pending candidates are stellar companions, with ~5 being potentially substellar and requiring follow-up observations for confirmation. We also detected a 43 ± 9 M {sub Jup} and an 81 ± 5 M {sub Jup} companion that were previously reported. We found 34 of our targets have acceleration measurements detected using Hipparcos–Gaia proper motions. Of those, 58{sub -14}{sup +12}% of the 12 stars with imaged companion candidates have significant accelerations (χ{sup 2}>11.8), while only 23{sub -6}{sup +11}% of the remaining 22 stars with no detected companion have significant accelerations. The significance of the acceleration decreases with increasing companion separation. These young accelerating low-mass stars with companions will eventually yield dynamical masses with future orbit monitoring.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Transboundary effects from idealized regional geoengineering

Regional geoengineering, by reflecting sunlight over a very limited spatial domain, might be considered as a means to target specific regional impacts of climate change. One of the obvious concerns raised by such approaches is the extent to which the resulting effects would be detectable well beyond the targeted region (e.g. in neighbouring countries). A few studies have explored this question for targeted regions that are still comparatively large. We consider idealized simulations with increased ocean albedo over relatively small domains; the Gulf of Mexico (0.23% of Earth's surface) and over the Australian Great Barrier Reef (0.07%), both with negligible global radiative forcing. Applied over these very small domains, the only statistically significant non-local changes we find are some limited reduction on summer precipitation in Florida in the Gulf of Mexico case (adjacent to the targeted region). The lack of transboundary effects suggests that governance needs for such targeted interventions are quite distinct from those for more global sunlight reflection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Gas electroluminescence in a dual phase xenon-doped argon detector

Noble element detectors using argon or xenon as the detection medium are widely used in the searches for rare neutrino and dark matter interactions. Xenon doping in liquid argon can preserve attractive properties of an argon target while enhancing the detectable signals with properties of xenon. Here, in this work, we deployed a dual-phase liquid argon detector with up to 4% xenon doping in the liquid and studied its gas electroluminescence properties as a function of xenon concentration. At ∼2% xenon doping in liquid argon, we measured ∼34 ppm of xenon in the gas and observed ∼2.5 times larger electroluminescence signals using vacuum ultraviolet silicon photomultipliers than those in pure argon. Analysis of signals of different wavelengths confirms that the argon gas electroluminescence process is strongly modified by the addition of xenon. We propose an analytical model to describe the underlying energy transfer mechanism in argon-xenon gas mixtures. Lastly, the implications of this measurement for low-energy ionization signal detection will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗
DOE OSTI2021

Vehicle Tracking

The detection and tracking of vehicles moving at hypersonic speeds (> Mach 5) at altitudes of ~100 km has been of growing interest due to the ongoing development of hypersonic glide vehicles (HGVs) by the U.S., Russia and China. This challenge is notably different from traditional ballistic missile systems whose re-entry vehicles move at hypersonic velocities but at much higher altitudes on a ballistic course. Hypersonic glide vehicles are also designed to be able to make sudden turns along their flight path making prediction of their trajectories difficult. Their ability to maneuver, combined with their high velocity and altitude make them difficult to track. The combination of these characteristics presents a challenge for anyone attempting to defend against a hypersonic glide vehicle attack by generally shortening the time available to react. That reaction can involve moving potential targets or attempting to detect the hypersonic glide vehicle and intercept it before it reaches the intended target. Figure 1 compares the trajectory of hypersonic vehicles compared to that of a traditional ballistic missile.

42 ENGINEERING↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Blockchain based Communication Architectures with Applications to Private Security Networks

Existing communication protocols in high consequence security networks are highly centralized. While this naively makes the controls easier to physically secure, external actors require fewer resources to disrupt the system because there are fewer points in the system can be destroyed or interrupted without the entire system failing. We present a solution to this problem using a proof-of-work-based blockchain implementation built on MultiChain. We construct a test-bed network containing two types of data input: visual imagers and microwave sensor information. These data types are ubiquitous in perimeter intrusion detection security systems and allow a realistic representation of a real-world network architecture. The cameras in this system use an object detection algorithm to nd important targets in the scene. The raw data from the camera and the outputs from the detection algorithm are then placed in a transaction on the distributed ledger. Similarly, microwave data is used to detect relevant events and are placed in a transaction. These transactions are then bundled into blocks and broadcast to the rest of the network using the Bitcoin-based MultiChain protocol. We develop five tests to examine the security metrics of our network. We performed the five security metric test using different sized networks from 7 to 39 nodes to determine how the metrics scale with respect to size. We nd that when compared to a centralized architecture our implementation provides a resiliency increase that is expected from a blockchain-based protocol without slowing the system so much that a human operator would notice. Furthermore, our approach is able to detect tampering in real time. Based on these results, we theorize that security networks in general could use a blockchain-based approach in a meaningful way.

97 MATHEMATICS AND COMPUTING↗