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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2023

Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps

Abstract Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity often necessitate labor-intensive manual model building and fitting into cryo-EM maps. In this work, we designed a protein model-building workflow, which combines a deep-learning cryo-EM map feature enhancement tool, CryoFEM (Cryo-EM Feature Enhancement Model) and AlphaFold. A benchmark test using 36 cryo-EM maps shows that CryoFEM achieves state-of-the-art performance in optimizing the Fourier Shell Correlations between the maps and the ground truth models. Furthermore, in a subset of 17 datasets where the initial AlphaFold predictions are less accurate, the workflow significantly improves their model accuracy. Our work demonstrates that the integration of modern deep learning image enhancement and AlphaFold may lead to automated model building and fitting for the atomistic interpretation of cryo-EM maps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Interrelationship between sex, age, blood volume and VO 2 max

Background Circulating blood volume (BV) and maximal oxygen uptake (VO 2 max) are physiological characteristics important for optimal human performance in aerospace and military operational environments. Addressing the interrelationships between BV, VO 2 max, sex, and age together is relevant to identifying those individuals with greatest risk of poor outcomes from hemorrhage, as well as assessing the levels of performance competence of individuals deployed in operational environments. In the present investigation, we report and compare for the first time BV and VO 2 max in a cohort of males and females together to test the hypothesis that BV and VO 2 max is lower in older people independent of sex. Methods To accomplish this, a ‘data mining’ effort of an historic database generated from NASA and U.S. Air Force experiments was conducted. BV, red cell volume, plasma volume, hematocrit, and VO 2 max were measured in 94 healthy individuals (28 females, 66 males) across an age range of 23 to 65 years to assess the interrelationship between sex, age, BV, and VO 2 max. Subjects were classified in age groups by < 40 years and ≥ 40 years; these groups identified females as pre‐ vs. post‐menopausal. Results Consistent with our hypothesis, comparisons revealed that males had higher (p < 0.01) BV, red cell volume, hematocrit, and VO 2 max than females when standardized for body size. Against expectations, BV was not different in older compared with younger males and females. VO 2 max was not different in older compared with younger females, while VO 2 max was lower in older males (p < 0.01). Conclusion We conclude that physiological mechanisms other than BV associated with aging appear to be responsible for a decline in VO 2 max of our older male subjects. Furthermore, factors other than menopause may also influence the control of BV in the female population. Our results provide evidence that aging may not compromise males or females in scenarios where BV can affect performance in aerospace and military environments. Support or Funding Information This research was supported in part by appointment of Ms. Koons to the Postgraduate Research Fellowship Program at the US Army Institute of Surgical Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and USAMRMC. Study funding was provided by a grant from the US Army Combat Casualty Care Research Program (D‐009‐2014‐USAISR). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Convertino, Victor↗
DOE OSTI2023

Spin-squeezed Gottesman-Kitaev-Preskill codes for quantum error correction in atomic ensembles

Gottesman-Kitaev-Preskill (GKP) codes encode a qubit in displaced phase-space combs of a continuous-variable (CV) quantum system and are useful for correcting a variety of high-weight photonic errors. Here we propose atomic ensemble analogs of the single-mode CV GKP code by using the quantum central limit theorem to pull back the phase-space structure of a CV system to the compact phase space of a quantum spin system. We study the optimal recovery performance of these codes under error channels described by stochastic relaxation and isotropic ballistic dephasing processes using the diversity combining approach for calculating channel fidelity. Additionally, we find that the spin GKP codes outperform other spin system codes such as cat codes or binomial codes. Our spin GKP codes based on the two-axis countertwisting interaction and superpositions of SU(2) coherent states are direct spin analogs of the finite-energy CV GKP codes, whereas our codes based on one-axis twisting do not yet have well-studied CV analogs. A state preparation scheme for the spin GKP codes is proposed which uses the linear-combination-of-unitaries method, applicable to both the CV and spin GKP settings. Finally, we discuss a fault-tolerant approximate gate set for quantum computing with spin-GKP-encoded qubits, obtained by translating gates from the CV GKP setting using the quantum central limit theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Ultrafast exciton and trion dynamics in highly 𝑛-doped Mo⁢S 2 monolayers: Many-body effects

Understanding many-body interactions of excitons and charge carriers in monolayer semiconductors is crucial for tuning their unique optical properties and optimizing their performance in optoelectronic devices. However, the sensitivity of these atomically thin semiconductors to doping, defects, and strain–arising from synthesis, substrate, and environmental conditions–hinders consistent observation of many-body effects. In this work, we employed linear and ultrafast transient optical absorption spectroscopy to investigate the influence of background doping on exciton many-body interactions in Mo⁢S 2 monolayers. Using reversible molecular physisorption gating, we achieved a high background doping density of 4.9 × 10 13 c⁢m −2 in an argon environment, which is significantly higher than those attainable with conventional electrical gating. Our results reveal a photoinduced 𝐴-exciton resonance redshift, attributed to band-gap renormalization at a low background-doping density of 4.3 × 10 12 c⁢m −2 in an air environment, transitioning to a blueshift at a high background-doping density of 4.9 × 10 13 c⁢m −2 due to dominant Pauli blocking effects and vertical excitation shifts. We further observed transient energy splitting between free-exciton and -trion states up to 57 meV due to exciton-electron interactions. The ultrafast spectroscopy further revealed exciton and trion dynamics, including fast energy splitting of exciton and trion resonances within 1 picosecond (ps) followed by a rapid decay having a lifetime of ∼5.4 ps. Furthermore, our results demonstrate the critical role of background-doping conditions in tuning many-body interactions and quasiparticle dynamics in 2D semiconductors, providing valuable insights for future device design and material engineering.

Doped semiconductors↗
DOE OSTI2022

End-to-end jet classification of boosted top quarks with the CMS open data

Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of 0.975 ± 0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824 ± 0.0013, serving as the first performance benchmark for these CMS open data samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Key role of antibonding electron transfer in bonding on solid surfaces

The description of the chemical bond between a solid surface and an atom or a molecule is the fundamental basis for understanding surface reactivity and catalysis. Despite considerable research efforts, the physics that rules the strength of such chemical bonds remains elusive, especially on semiconductor surfaces. Widespread understandings are mostly based on the degree of filling of antibonding surface-adsorbate states that weaken the surface adsorption. The unoccupied antibonding surface-adsorbate states are often considered to have no effects on surface bonding. Here in this paper, we show that the energy levels of unoccupied antibonding surface-adsorbate states relative to the Fermi-level play a critical role in determining the trends in variations of surface adsorption energies. The electrons that would occupy those high-energy antibonding states are transferred to the Fermi-level, leading to an energy gain that largely controls surface bonding. To illustrate this picture, as a validating case, we study the hydrogen evolution reaction (HER) catalyzed by MoS 2 from density functional theory calculations. We find that the majority of antibonding surface-hydrogen states are positioned well above the Fermi-energy. A clear linear relationship between the energy gain from antibonding electron transfer and the adsorption energy is identified for hydrogen binds to either molybdenum or sulfur atoms at different sites. The antibonding-electron transfer energy can thus serve as a primary catalytic activity descriptor. The emerging picture identifies the origin of HER on MoS 2 , which is related to the empty in-gap states induced by sulfur vacancies or edges. Under this picture, the effects of surface inhomogeneity (e.g., defects, step edges) on surface bonding strength can be understood. This antibonding electron transfer picture also offers a physically different explanation for the well-known d-band theory for hydrogen adsorption on transition metal surfaces. The results provide guidelines for understanding and optimizing catalyst performance and designing new solid catalysts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Enhanced magnetostriction through dilute Ce doping of Fe-Ga

Doping of magnetostrictive galfenol (Fe 82 Ga 18 , in at.%) with rare-earth elements significantly enhances magnetostriction, with the largest gains achieved in textured melt-spun ribbons. Here, it is demonstrated that even extremely dilute Ce, as little as 65 ppm, can double the magnetostrictive response of galfenol when coupled with an appropriate heat treatment. This improvement is correlated with a compression of the host lattice, both of which reach their maximum extent at the calculated solubility limit of Ce in in body-centered cubic (bcc) galfenol, ~ 50 ppm. Beyond this point, excess Ce segregates into CeGa 2 , which forms an interdendritic network throughout the sample at high Ce levels and cannot be resolutionized through heat treatments. These findings point to the importance of solubility limits (i.e., equilibrium thermodynamics) in determining appropriate doping levels or heat treatment couples to optimize magnetostrictive performance, confirming that overdoping is actively detrimental to both material properties and cost.

36 MATERIALS SCIENCE↗
DOE OSTI2021

rmc-discord : reverse Monte Carlo refinement of diffuse scattering and correlated disorder from single crystals

A user-friendly program has been developed to analyze diffuse scattering from single crystals with the reverse Monte Carlo method. The approach allows for refinement of correlated disorder from atomistic supercells with magnetic or structural (occupational and/or displacive) disorder. The program is written in Python and optimized for performance and efficiency. Refinements of two user cases obtained with legacy neutron-scattering data demonstrate the effectiveness of the approach and the developed program. It is shown with bixbyite, a naturally occurring magnetic mineral, that the calculated three-dimensional spin-pair correlations are resolved with finer real-space resolution compared with the pair distribution function calculated directly from the reciprocal-space pattern. With the triangular lattice Ba 3 Co 2 O 6 (CO 3 ) 0.7 , refinements of occupational and displacive disorder are combined to extract the one-dimensional intra-chain correlations of carbonate molecules that move toward neighboring vacant sites to accommodate strain induced by electrostatic interactions. The program is packaged with a graphical user interface and extensible to serve the needs of single-crystal diffractometer instruments that collect diffuse-scattering data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Nanoporous gold: a hierarchical and multiscale 3D test pattern for characterizing X-ray nano-tomography systems

Full-field transmission X-ray microscopy (TXM) is a well established technique, available at various synchrotron beamlines around the world as well as by laboratory benchtop devices. One of the major TXM challenges, due to its nanometre-scale resolution, is the overall instrument stability during the acquisition of the series of tomographic projections. The ability to correct for vertical and horizontal distortions of each projection image during acquisition is necessary in order to achieve the effective 3D spatial resolution. The effectiveness of such an image alignment is also heavily influenced by the absorption properties and strong contrast of specific features in the scanned sample. Here it is shown that nanoporous gold (NPG) can be used as an ideal 3D test pattern for evaluating and optimizing the performance of a TXM instrument for hard X-rays at a synchrotron beamline. Unique features of NPG, such as hierarchical structures at multiple length scales and high absorbing capabilities, makes it an ideal choice for characterization, which involves a combination of a rapid-alignment algorithm applied on the acquired projections followed by the extraction of a set of both 2D- and 3D-descriptive image parameters. This protocol can be used for comparing the efficiency of TXM instruments at different synchrotron beamlines in the world or benchtop devices, based on a reference library of scanned NPG samples, containing information about the estimated horizontal and vertical alignment values, 2D qualitative parameters and quantitative 3D parameters. The possibility to tailor the ligament sizes of NPG to match the achievable resolution in combination with the high electron density of gold makes NPG an ideal 3D test pattern for evaluating the status and performance of a given synchrotron-based or benchtop-based TXM setup.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff

Exponentially increasing data volumes, coupled with new modes of analysis have created significant new opportunities for data scientists. However, the stochastic nature of many data science techniques results in tradeoffs between costs and accuracy. For example, machine learning algorithms can be trained iteratively and indefinitely with diminishing returns in terms of accuracy. In this paper we explore the cost-accuracy tradeoff through three representative examples: we vary the number of models in an ensemble, the number of epochs used to train a machine learning model, and the amount of data used to train a machine learning model. We highlight the feasibility and benefits of being able to measure, quantify, and predict cost-accuracy tradeoffs by demonstrating the presence and usability of these tradeoffs in two different case studies.

big data analytics↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗
DOE OSTI2016

Dynamic Braking System of a Tidal Generator

Renewable energy generation has experienced significant cost reductions during the past decades, and it has become more accepted by the global population. In the beginning, wind generation dominated the development and deployment of renewable energy; however, during recent decades, photovoltaic (PV) generation has grown at a very significant pace due to the tremendous decrease in the cost of PV modules. The focus on renewable energy generation has now expanded to include new types with promising future applications, such as river and tidal generation. The input water flow to these types of resources is more predictable than wind or solar generation. The data used in this paper is representative of a typical river or tidal generator. The analysis is based on a generator with a power rating of 40 kW. The tidal generator under consideration is driven by two sets of helical turbines connected to each side of the generator located in between the turbines. The generator is operated in variable speed, and it is controlled to maximize the energy harvested as well as the operation of the turbine generator. The electrical system consists of a three-phase permanent magnet generator connected to a three-phase passive rectifier. The output of the rectifier is connected to a DC-DC converter to match the rectifier output to the DC bus voltage of the DC-AC inverter. The three-phase inverter is connected to the grid, and it is controlled to provide a good interface with the grid. One important aspect of river and tidal generation is the braking mechanism. In a tidal generator, the braking mechanism is important to avoid a runaway condition in case the connection to the grid is lost when there is a fault in the lines. A runaway condition may lead to an overspeed condition and cause extreme stresses on the turbine blade structure and eventual disintegration of the mechanical structure. In this paper, the concept of the dynamic braking system is developed and investigated for normal and abnormal operations. The main objective is to optimize the performance under emergency braking while designing the system to be as simple as possible to avoid overdesigning the power electronics or exceeding the target budget.

13 HYDRO ENERGY↗
DOE OSTI2025

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗
DOE OSTI2016

Investigating the Impact of Wind Turbines on Distribution System Stability

Modern wind turbines utilize power electronic converters to regulate their output and optimize their performance. Their impact on the distribution system is not as well understood as for transmission system. The novelty of this work is in studying the impact of wind turbines given its proximity to faults or severe voltage events, and the influence on system stability given its location relative to the substation (representing the conventional grid). This paper presents the frequency and voltage swing plots for various study scenarios. The responses are analyzed two fold - Steady state operation, and performance given a fault or voltage events occurs in the system. The findings are presented, with crucial differences from transmission systems highlighted.

17 WIND ENERGY↗
DOE OSTI2019

All-Optical Frequency Processor for Networking Applications

Herein, we propose an electro-optic approach for transparent optical networking, in which frequency channels are actively transformed into any desired mapping in a wavelength-multiplexed environment. Based on electro-optic phase modulators and Fourier-transform pulse shapers, our all-optical frequency processor (AFP) is examined numerically for the specific operations of frequency channel hopping and broadcasting, and found capable of implementing these transformations with favorable component requirements. Extending our analysis via a mutual-information–based metric for system optimization, we show how to optimize transformation performance under limited resources in a classical context, contrasting the results with those found using metrics motivated by quantum information, such as fidelity and success probability. Given its compatibility with on-chip implementation, as well as elimination of optical-to-electrical conversion in frequency channel switching, the AFP looks to offer valuable potential in silicon photonic network design, as well as the realization of high-dimensional frequency-bin gates.

42 ENGINEERING↗