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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Security Analysis of a Class of Secured Spread Spectrum Systems

Abstract—A method of adding physical layer security to a class of spread spectrum systems has been recently proposed. In this paper, we look into the rate at which an eavesdropper may gain information about the system to decipher the data symbols. The Shannon mutual information is used to measure the rate of information that may be gained by an eavesdropper. The k-nearest neighbors (k-NN) method is used to obtain the estimates of relevant entropy values which will be then used to quantify the rate of information recovery as more data are being transmitted. It turns out that such information recovery requires adoption of special methods that avoid any destructive bias in the estimates. Details of these methods are also presented.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks

Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and physics-based models provides information about spatially resolved neutron scattering length density (NSLD) profiles. Recent improvements in data acquisition systems have allowed acquiring thousands of NR curves in a couple of hours, which has led to a need for automated data analysis tools to interpret NR measurements in real-time. Here, we present a machine learning analysis workflow that uses a series of models, based on a convolutional neural network (CNN), to learn the relation between the NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly from NRs. The usefulness of our CNN-based models is demonstrated by constructing NSLDs from NRs of several films containing homopolymer polyzwitterions and diblock copolymers mixed with different types of salts. Comparisons of the NSLDs with those constructed using ad hoc multi-layer models reveal a very good agreement, suggesting the potential of CNN-based models for real-time automated data analysis of NRs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Difficulties with late-time solutions for the Hubble tension

We explore the notion that cosmological models that modify the late-time expansion history cannot simultaneously fit the SH0ES Collaboration’s measurements of the Hubble constant, Dark Energy Spectroscopic Instrument baryon acoustic oscillations data, and type Ia supernova distances. Adopting a few simple phenomenological models, we quantitatively demonstrate that a satisfactory fit with a model with late-time expansion history can only be achieved if one of the following is true: (1) there is a sharp step in the absolute magnitude of type Ia supernovae at very low redshift, 𝑧 ∼0.01, or (2) the distance duality relation, 𝑑 𝐿 ⁡(𝑧) =(1+𝑧) 2 ⁢𝑑 𝐴 ⁡(𝑧), is broken. Both solutions are trivial in that they effectively decouple the calibrated type Ia supernovae measurements from other data, and this qualitatively agrees with previous work built on studying specific dark-energy models. We also identify a less effective class of late-time solutions with a transition at 𝑧 ≃0.15 that lead to a more modest improvement in fit to the data than models with a very low-𝑧 transition. Our conclusions are largely unchanged when we include surface brightness fluctuation distance measurements, with their current systematic uncertainties, to our analysis. Here, we finally illustrate our findings by studying a physical model which, when equipped with the ability to smoothly change the absolute magnitude of type Ia supernovae, partially resolves the Hubble tension.

Cosmological parameters↗
DOE OSTI2025

Polaris-PARCS Sensitivity Study on LWR Fuel Cycles: Polaris Input Options

This study is the first of a multi-phase effort to assess the sensitivity of light-water reactor (LWR) core-level prediction biases to changes in lattice-level calculation parameters. Prediction bias is the measured-to-predicted difference in a core-level quantity of interest (QOI) which can be estimated by comparing the simulation results with the plant-measured data for key nuclear parameters. The LWR two-step neutronics codes employed herein are the SCALE–Polaris lattice physics code (v6.3.1) and the Purdue Advanced Reactor Core Simulator (PARCS) nodal diffusion simulator (v3.4.2), both funded and used for confirmatory analysis to support licensing by the US Nuclear Regulatory Commission (NRC). Polaris–PARCS is used to model Watts Bar Unit 1 cycles 1–3 and Peach Bottom Unit 2 cycles 1–3. This study focuses on the impact of changes to Polaris input options such as scattering treatment or quadrature settings and how these input options induce changes in core-level quantities of interest (QOIs)bias. The report documents multiple bias assessments for different modeling choices and compares the bias magnitude to the QOI measurement uncertainties. Future companion reports will investigate the sensitivity of core-level LWR prediction bias to Polaris input options and Polaris-computed QOIs such as few-group assembly-homogenized cross sections to gain an understanding of the key drivers of prediction bias at lattice and core levels for application of a two-step LWR neutronics procedure in a licensing scenario.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Machine Learning Neutrino-Nucleus Cross Sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section—leveraging only Standard-Model symmetries— can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗
DOE OSTI2025

Toroidal Alfven Wave Coupling (Nonlinear Wave-Wave Interactions) on DIII-D

Connect DIII-D physics with space plasma phenomena. In this case of using the toroidal Alfvén eigenmodes and frequency-chirping Reversed-Shear Alfvén eignmodes in DIII-D, we will document how the nonlinear interactions among dipolar Kinetic Alfvén Wave eigenmodes in space plasmas may determine saturation levels of these fluctuations. We seek evidence of nonlinear energy transfer and wave-wave coupling during 3-wave interactions mediated by a much lower-frequency mode. In FY2019, we found evidence of nonlinear “wave-wave” interactions in 175 relevant shots of archival DIII-D data. Toroidal mode number was identified and spectrograms were produced from each shot’s Mirnov coil data. Bispectral analysis was performed using a preliminary version of a new user friendly code derived from a 1995 M.S. thesis at WVU. These results formed a part of a May 2019 M.S. thesis at WVU.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

OzDES Reverberation Mapping of Active Galactic Nuclei: Final Data Release, Black-Hole Mass Results, & Scaling Relations

Over the last decade, the Australian Dark Energy (OzDES) collaboration has used Reverberation Mapping to measure the masses of high redshift supermassive black holes. Here we present the final review and analysis of this OzDES reverberation mapping campaign. These observations use 6-7 years of photometric and spectroscopic observations of 735 Active Galactic Nuclei (AGN) in the redshift range 0.13-3.85 and bolometric luminosity range 44.3 - 47.5 erg/s. Both photometry and spectra are observed in visible wavelengths, allowing for the physical scale of the AGN broad line region to be estimated from reverberations of the H{̱e̱ṯa̱}̱, MgII and CIV emission lines. We successfully use reverberation mapping to constrain the masses of 62 super-massive black holes, and combine with existing data to fit a power law to the lag-luminosity relation for the H{̱e̱ṯa̱}̱ and MgII lines with a scatter of ~0.25 dex, the tightest yet identified, fit specifically for consistency with high redshift AGN. We fit a similarly constrained relation for CIV, resolving a tension with the low luminosity literature AGN by accounting for selection effects arising from finite survey length. We also examine the impact of emission line width and luminosity (related to accretion rate) in reducing the scatter of these scaling relationships and find no significant improvement over the lag-only approach for any of the three lines. Using these relations, we further estimate the masses and accretion rates of 246 AGN with single epoch methods. We also use these relations to estimate the relative sizes of the H{̱e̱ṯa̱}̱, MgII and CIV emitting regions. In short, we provide a comprehensive benchmark of high redshift AGN reverberation mapping at the close of this most recent generation of surveys, including light curves, time-delays, and a set of significantly improved radius-luminosity relations for use with high-redshift populations.

McDougall, Hugh [Queensland U.]↗
DOE OSTI2024

The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017

The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a unique registryID, based on the U.S. Environmental Protection Agency (EPA) Facility Registry Service, and includes its coordinates and other geographic identifiers. Energy-using units are characterized by design capacity, as well as their estimated energy use, greenhouse gas emissions, and physical throughput using 2017 data from the EPA's National Emissions Inventory and Greenhouse Gas Reporting Program. An overview of the derivation methods is provided in a separate technical report which will be linked after publication. The Python code used to compile the dataset is available in a GitHub repository. An updated 2020 version is under development.

Array↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2024

EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

New dark matter analysis of milky way dwarf satellite galaxies with madhatv2

We obtain bounds on dark matter annihilation using 14 years of publicly available Fermi-LAT data from a set of 54 dwarf spheroidal galaxies, using spectral information from 16 energy bins. We perform this analysis using our updated and publicly available code , which can be used to test a variety of models for dark matter particle physics and astrophysics in an accessible manner. In particular, we note that including Carina III in the analysis strengthens constraints on s -wave annihilation into two-body Standard Model final states by a factor of ∼ 3 but broadens the error on the constraint due to the large uncertainty of its J -factor. Our findings illustrate the importance of verifying if Carina III is in fact a dwarf spheroidal galaxy and measuring more precisely its J -factor. More generally, they highlight the significance of forthcoming discoveries of nearby ultrafaint dwarfs for dark matter indirect detection. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
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

Constructing Data-Driven Predictions at the Far Detector for NOvA's Neutrino Oscillation Analysis.

NOvA, is a two-detector, long-baseline neutrino oscillation experiment located at Fermilab, Batavia, IL, USA. It is designed primarily to constrain neutrino oscillation parameters using $\nu_\mu \ (\bar{\nu}_\mu)$ disappearance and $\nu_e \ (\bar{\nu}_e)$ appearance data. The Neutrinos at Main Injector (NuMI) beamline at Fermilab provides a high purity 900 KW intense beam of neutrinos and anti-neutrinos to NOvA. The NOvA Near Detector, located 100m underground and 1km away from the beam source, observes the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and beam $\nu_e \ (\bar{\nu}_e)$ event spectrum. The Far Detector, located in Ash River, MN, USA, is 809 km from the ND and records the oscillated $\nu_e \ (\bar{\nu}_e)$ and the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ event spectrum. NOvA uses a data-driven technique called extrapolation to predict the expected number of $\nu_\mu \ (\bar{\nu}_\mu)$ and $\nu_e \ (\bar{\nu}_e)$ events at the Far Detector using the Near Detector data. The use of data from a functionally equivalent Near Detector provides a powerful constraint on the systematic uncertainties in NOvA neutrino oscillation analyses. As NOvA continues to add data statistics, a robust constraint on systematics becomes more crucial for neutrino oscillation analysis. The details of the NOvA neutrino oscillation analysis framework and how it constrains dominant systematic uncertainties using the Near Detector data will be discussed in this poster.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Laser calibration system at ProtoDUNE-HD

The Deep Underground Neutrino Experiment (DUNE) is a full experiment consisting of multiple detectors separated by a near and far site. DUNE will study long-baseline neutrino oscillations, which will provide insight into CP-violation, neutrino mass ordering, and the matter/antimatter asymmetry. Additionally, DUNE will search for nucleon decay and observe neutrinos from supernovae. DUNE relies on liquid argon time projection chambers (LAr TPCs), an excellent technology for tracking particles and reconstructing their interactions with high precision. To achieve its goals, DUNE is supported by the ProtoDUNE experiments at CERN, which serve as large-scale prototypes to validate detector technologies and calibration systems. The precise calibration of the electric field within the detector is vital for accurate 3D reconstruction of particle tracks, particularly in maintaining the consistency of charge measurements along the drift path. This work presents the development and implementation of a laser calibration system designed for ProtoDUNE-II. This poster involved the physical installation and commissioning of the laser system. The commissioning process included extensive testing to ensure alignment and operational efficiency, such as rotating the laser to observe behavior of tracks. The groundwork established during these steps is crucial for future data analysis, aiming to measure the electron lifetime (in the active volume) and map the electric field inside the detector with high precision.

Campanelli, Wallison [LIP]↗
DOE OSTI2025

Using "AI Poincare" to analyze non-linear integrable optics

This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.

Osmanov, Lazare [Free U. Tbilisi]↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
DOE OSTI2024

Real-time Anomaly Detection at the L1 Trigger of CMS Experiment

We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a prediction for each event within these constraints, which can be used to select anomalous events for further analysis. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough testing of new trigger algorithms on live data, but without interrupting data taking. We describe the methodology to achieve ultra low latency anomaly detection, and present the integration of the DNN into the GT test crate, as well as the monitoring, testing, and validation of the algorithm during proton collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗