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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

Recommended Nuclear Structure and Decay Data for A=206 Isobars

Here, evaluated nuclear structure and decay data for all nuclei with mass number A=206 ( 206 Pt, 206 Au, 206 Hg, 206 Tl, 206 Pb, 206 Bi, 206 Po, 206 At, 206 Rn, 206 Fr, 206 Ra and 206 Ac), are presented. All available experimental data are compiled and evaluated, and best values for level and γ-ray energies, quantum numbers, lifetimes, γ-ray intensities and transition probabilities, as well as other nuclear properties, are recommended. Inconsistencies and discrepancies that exist in the literature are discussed. A number of computer codes (https://www-nds.iaea.org/public/ensdf_pgm/) developed by members of the NSDD network were used during the evaluation process. This work supersedes the earlier evaluation by F.G. Kondev (2008Ko21), published in Nuclear Data Sheets 109, 1527 (2008).

Kondev, F. G. [Argonne National Laboratory (ANL), ↗
NASA NTRS1984

Theoretical studies of X-ray emission from supernova remnants and other non-equilibrium astrophysical plasmas

To improve the accuracy of thermal X-ray emission spectrum predictions, new computations of electron excitation rates, dielectric recombination rates, and updated codes were applied to existing data on supernova remnants and O star winds. Because of its general predominance along with low ionization and recombination rates, Fe(XVII) was analyzed with the inclusion of resonances to the excitation rate data. Corrections to the existing dielectric recombination rates based on detailed Fe (XVII) calculations and the more approximate Quantum Defect Method were accomplished. by using Distorted Wave ionization rates and reliable excitation rates, models for X-ray, ultraviolet, and optical emission line intensities of non-radiative shock wave can be constructed that supply data on shock velocity, preshock ionization states, and elemental abundances. The radiative driving force that drives the shock wave is calculated through line opacities of the shocked gas. X-ray emission spectra are also calculated for O star winds.

Dupree, A. K.↗
DOE OSTI2025

Improving the efficiency of learning-based error mitigation

Error mitigation will play an important role in practical applications of near-term noisy quantum computers. Current error mitigation methods typically concentrate on correction quality at the expense of frugality (as measured by the number of additional calls to quantum hardware). To fill the need for highly accurate, yet inexpensive techniques, we introduce an error mitigation scheme that builds on Clifford data regression (CDR). The scheme improves the frugality by carefully choosing the training data and exploiting the symmetries of the problem. We test our approach by correcting long range correlators of the ground state of XY Hamiltonian on IBM Toronto quantum computer. We find that our method is an order of magnitude cheaper while maintaining the same accuracy as the original CDR approach. The efficiency gain enables us to obtain a factor of 10 improvement on the unmitigated results with the total budget as small as 2 ⋅ 10 5 shots. Furthermore, we demonstrate orders of magnitude improvements in frugality for mitigation of energy of the LiH ground state simulated with IBM's Ourense-derived noise model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantum adversarial learning for kernel methods

We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defense strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2018

Quantum Machine Learning

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Biswas, Rupak↗
DOE OSTI2025

Coupling to rotational manifolds to improve gas-phase pump–probe spectroscopic models

The physical picture of gas-phase optical transitions is normally presented as an isolated two-level system balanced by upward and downward processes. Isolated models assume a phenomenological treatment of collisional dephasing but do not strictly account for collisional population exchange with the rotational baths. While this assumption is valid under low-intensity conditions, where excitation is rate-limiting, isolated models can deviate from Beer’s Law at sufficient pressures and monochromatic intensities when both collisional broadening and power broadening are comparable to (or greater than) lifetime broadening, which are not uncommon conditions for cavity enhanced spectroscopies in the mid-IR spectral range. Although this problem has been addressed by rate-equation models for linear absorption measurements, a general treatment for multi-level quantum mechanical models suitable for non-linear absorption measurements (two-photon/two-color/pump–probe) is lacking. Isolated models require physical parameter inputs that disagree with expected values by at least an order of magnitude. These non-physical models undermine the ability to predict non-linear signal strengths under untested conditions and thereby limit the potential to optimize the sensitivity of non-linear spectroscopies and to expand their analytical applications (e.g., new analytes and/or buffer gases, changes in cavity free-spectral-range, changes in intracavity powers or wavelengths, and accurate investigation of physical phenomena). In this study, we derive bath-coupled models for gaseous pump–probe spectroscopy by application of the quantum Lindblad equation and detailed balance. Bath-coupled models are shown to fit data consistently across variations in intensity and agree with all physically expected values.

Cavity ring-down spectroscopy↗
DOE OSTI2025

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Mavian, Alex (ORCID:0000000279448830)↗
DOE OSTI2025

From Data to Discovery: AI's Transformative Role in Thin Film Research

The advancement of thin film technologies is pivotal for progress in numerous fields, including energy, electronics, and quantum computing. However, the traditional trial-and-error approach to materials discovery is inherently slow and inefficient. This presentation will showcase how artificial intelligence (AI) is transforming thin film research by enabling a data-driven paradigm shift. We will highlight our past successes in applying AI to understand radiation damage in thin film oxides, demonstrating how graph analytics can unravel complex material behavior. Additionally, we will provide insights into our current work at the National Renewable Energy Laboratory, where we are leading the charge in autonomous materials science. Backed by a $14M investment in our characterization facility, we are developing AI-guided workflows that seamlessly integrate experimentation and AI-guided decision-making. By harnessing the power of AI, we aim to accelerate the discovery and design of high-performance thin films, propelling innovation across a multitude of industries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bottomonium suppression in 5.02 and 8.16 TeV 𝑝-Pb collisions

Here, we compute the suppression of ϒ⁡(1⁢𝑆), ϒ⁡(2⁢𝑆), and ϒ⁡(3⁢𝑆) states in 𝑝-Pb collisions relative to 𝑝⁢𝑝 collisions, including nuclear parton distribution function (nPDF) effects, coherent energy loss, momentum broadening, and final-state interactions in the quark-gluon plasma. We employ the EPPS21 nPDFs and calculate the uncertainty resulting from variation over the associated error sets. To compute coherent energy loss and momentum broadening, we follow the approach of Arleo, Peigne, and collaborators. The 3+1⁢D viscous hydrodynamical background evolution of the quark-gluon plasma is generated by anisotropic hydrodynamics. The in-medium suppression of bottomonium in the quark-gluon plasma is computed using a next-to-leading-order open quantum system framework formulated within potential nonrelativistic quantum chromodynamics. We find that inclusion of all these effects provides a reasonable description of experimental data from the ALICE, ATLAS, CMS, and LHCb Collaborations for the suppression of ϒ⁡(1⁢𝑆), ϒ⁡(2⁢𝑆), and ϒ⁡(3⁢𝑆) as a function of both transverse momentum and rapidity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
NASA NTRS2009

Multi-wavelength time-coincident optical communications system and methods thereof

An optical communications transmitter includes a oscillator source, producing a clock signal, a data source, producing a data signal, a modulating circuit for modulating the clock signal using the data signal to produce modulating signals, optical drivers, receiving the modulating signals and producing optical driving signals based on the modulating signals and optical emitters, producing small numbers of photons based on the optical driving signals. The small numbers of photons are time-correlated between at least two separate optical transmission wavelengths and quantum states and the small number of photons can be detected by a receiver to reform the data signal.

Lekki, John↗
NASA NTRS2012

Revisions to Photochemical Data for Use in Atmospheric Modeling

Solar and stellar flux incident on an atmosphere can cause molecules to dissociate into highly reactive species and allows for photochemical processes to play a fundamental role in atmospheric chemistry. While models have tried to simulate such processes, they are extremely sensitive to photoabsorption cross-sections and quantum yields: two parameters that are important in determining the photodissociation rate, and hence the lifetime, of atmospheric compounds. Obtaining high-resolution and current data for these parameters is therefore highly desirable. Due to this, database and literature searches for high-quality cross-sections and quantum yields were performed and compiled for KINETICS, a Caltech/JPL Chemical Transport Model that can be used in modeling planetary atmospheres. Furthermore, photodissociation rates determined by running a Titan 1-D model were used to verify the completeness of these latest revisions.

Chemical Transport Model (CTM)↗
DOE OSTI2025

Consistent thermodynamic properties for alicyclic components of jet fuels: Experimental data, estimation methods, and homologous series trends

Alkylcycloalkanes represent a significant fraction of jet fuel components. An evaluation of their thermodynamic properties, enthalpies of formation in liquid and gas phases and enthalpies of vaporization, was conducted. A combination of available experimental data, up-to-date group-contribution methods, high-level quantum-chemical calculations, and homologous series trends was used to identify outliers and to recommend the most reliable values. The group-contribution approach was found to work well for the enthalpies of vaporization. Its performance for the enthalpies of formation in the liquid and gas phases was found to be substantially less effective, especially considering notable differences in this property among stereoisomers. Computationally affordable high-level ab initio results and homologous series trend analysis appeared more reliable. In conclusion, the recommended property values for 212 individual compounds and their isomeric mixtures were provided.

09 BIOMASS FUELS↗
DOE OSTI2025

Observation of nuclear modification of energy-energy correlators inside jets in heavy ion collisions

Energy-energy correlators are constructed by averaging the number of charged particle pairs within jets, weighted by the product of their transverse momenta, as a function of the angular separation of the particles within a pair. They are sensitive to a multitude of perturbative and nonperturbative quantum chromodynamics phenomena in high-energy particle collisions. Using lead-lead data recorded with the CMS detector, energy-energy correlators inside high transverse momentum jets are measured in heavy ion collisions for the first time. The data are obtained at a nucleon-nucleon center-of-mass energy of 5.02 TeV and correspond to an integrated luminosity of 1.70 nb −1 . A similar analysis is done for proton-proton collisions at the same center-of-mass energy to establish a reference. The ratio of lead-lead to proton-proton energy-energy correlators reveals significant jet substructure modifications in the quark-gluon plasma. The results are compared to different models that incorporate either color coherence or medium response effects, where the two effects predict similar substructure modifications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

TMD Handbook

This handbook provides a comprehensive review of transverse-momentum-dependent parton distribution functions and fragmentation functions, commonly referred to as transverse momentum distributions (TMDs). TMDs describe the distribution of partons inside the proton and other hadrons with respect to both their longitudinal and transverse momenta. They provide unique insight into the internal momentum and spin structure of hadrons, and are a key ingredient in the description of many collider physics cross sections. Understanding TMDs requires a combination of theoretical techniques from quantum field theory, nonperturbative calculations using lattice QCD, and phenomenological analysis of experimental data. The handbook covers a wide range of topics, from theoretical foundations to experimental analyses, as well as recent developments and future directions. It is intended to provide an essential reference for researchers and graduate students interested in understanding the structure of hadrons and the dynamics of partons in high energy collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1972

Joule-Thomson inversion curves and related coefficients for several simple fluids

The equations of state (PVT relations) for methane, oxygen, argon, carbon dioxide, carbon monoxide, neon, hydrogen, and helium were used to establish Joule-Thomson inversion curves for each fluid. The principle of corresponding states was applied to the inversion curves, and a generalized inversion curve for fluids with small acentric factors was developed. The quantum fluids (neon, hydrogen, and helium) were excluded from the generalization, but available data for the fluids xenon and krypton were included. The critical isenthalpic Joule-Thomson coefficient mu sub c was determined; and a simplified approximation mu sub c approximates T sub c divided by 6P sub c was found adequate, where T sub c and P sub c are the temperature and pressure at the thermodynamic critical point. The maximum inversion temperatures were obtained from the second virial coefficient (maximum (B/T)).

Hendricks, R. C.↗
NASA NTRS1972

Optical communication theory.

An introduction is provided to the important ideas involved in the design and the evaluation of optical communication systems which transmit digital or analog data. Field representations are discussed together with background noise, a detector statistical model, quantum receivers, and turbulent and scatter channels. Free-space channels are considered, taking into account heterodyne systems, preamplifier and homodyne systems, direct-detection systems, and optimum quantum systems.

Hoversten, E. V.↗
NASA NTRS1991

Direct solar pumping of semiconductor lasers: A feasibility study

The primary goals of the feasibility study are the following: (1) to provide a preliminary assessment of the feasibility of pumping semiconductor lasers in space directly focused sunlight; and (2) to identify semiconductor laser structures expected to operate at the lowest possible focusing intensities. It should be emphasized that the structures under consideration would provide direct optical-to-optical conversion of sunlight into laser light in a single crystal, in contrast to a configuration consisting of a solar cell or battery electrically pumping a current injection laser. With external modulation, such lasers may prove to be efficient sources for intersatellite communications. We proposed to develop a theoretical model of semiconductor quantum-well lasers photopumped by a broadband source, test it against existing experimental data where possible, and apply it to estimating solar pumping requirements and identifying optimum structures for operation for operation at low pump intensities. This report outlines our progress toward these goals. Discussion of several technical details are left to the attached summary abstract.

Anderson, Neal G.↗