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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

INTERFACES. A Program for Determining the 3D Structures of Surfaces Sites Using NMR Data

Dynamic nuclear polarization surface enhanced NMR spectroscopy has enabled the determination of high-resolution structures from surface-supported molecules, including singlesite heterogeneous catalysts. Structure determinations have largely mimicked the approaches used in biomolecular NMR spectroscopy, namely, using distance measurements to constrain a conformational search. These early demonstrations made use of purpose-built software, which has limited the adoption of the technique. Herein, we describe the open-source program INTERFACES (Interpret NMR to Elucidate or Reconstruct the Full Atomistic Configurations of External Surfaces) which automates the analysis of RE(SP)DOR data as well as the structure determination for surface sites. Distances, angles, dihedral angles, complex orientation, and distance from the support can all be sampled to find all structures that agree with the experimental data. A χ 2 metric is used to define the error ranges of the REDOR fits and produce structures with an arbitrary level of confidence. Structural solutions are then provided as both overlays and ORTEP-like probability ellipsoids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Brillouin Sensing with PCA, and PCA-Based Neural Networks for Efficient Temperature Monitoring

This work explores peak estimation techniques in Brillouin Optical Time Domain Analysis (BOTDA), emphasizing both accuracy and efficiency. Euclidean distance measurement method is applied to principal components derived from Brillouin Gain Spectrum data. It offers a major speed advantage being 180 170 times faster than traditional curve fitting methods such as Lorentzian curve fitting, while maintaining similar accuracy. Additionally, a PCA- based neural network model shows significant reduction of peak estimation time compared to Lorentzian fitting. Results show Brillouin frequency shift errors lie under 0.75 MHz in both Euclidean distance-based and neural network-based methods, both of which utilize PCA components. For large data sets and long length fibers, PCA- assisted neural network for peak estimation would be an efficient solution.

Distributed optical fiber sensing↗
DOE OSTI2022

Understanding Air Quality Changes after Implementation of Mitigation Measures during a Pandemic: A Scoping Review of Literature in the United States

Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict-traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM 2.5 , PM 10 ), ozone (O 3 ), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quasar UV/X-ray relation luminosity distances are shorter than reverberation-measured radius–luminosity relation luminosity distances

ABSTRACT We use measurements of 59/58 quasars (QSOs), over a redshift range 0.0041 ≤ z ≤ 1.686, to do a comparative study of the radius–luminosity (R − L) and X-ray−UV luminosity (LX − LUV) relations and the implication of these relations for cosmological parameter estimation. By simultaneously determining R − L or LX − LUV relation parameters and cosmological parameters in six different cosmological models, we find that both R − L and LX − LUV relations are standardizable but provide only weak cosmological parameter constraints, with LX − LUV relation data favouring larger current non-relativistic matter density parameter Ωm0 values than R − L relation data and most other available data. We derive LX − LUV and R − L luminosity distances for each of the sources in the six cosmological models and find that LX − LUV relation luminosity distances are shorter than R − L relation luminosity distances as well as standard flat ΛCDM model luminosity distances. This explains why LX − LUV relation QSO data favour larger Ωm0 values than do R − L relation QSO data or most other cosmological measurements. While our sample size is small and only spans a small z range, these results indicate that more work is needed to determine whether the LX − LUV relation can be used as a cosmological probe.

Astronomy & Astrophysics↗
DOE OSTI2023

The use of double-mode RR Lyrae stars as robust distance and metallicity indicators

Abstract RR Lyrae stars are one of the primary distance indicators for old stellar populations such as globular clusters, dwarf galaxies and galaxies. Typically, fundamental-mode RR Lyr stars are used for distance measurements, and their accuracy is strongly limited by the dependence of absolute magnitudes on metallicity, in both the optical and infrared bands. Here, we report the discovery of a period–(period ratio)–metallicity relation for double-mode RR Lyr stars, which can predict metallicity as accurately as the low-resolution spectra. With theoretical and observational evidence, we propose that the period–luminosity relation of double-mode RR Lyr stars is not affected by the metallicity. Combining the Large Magellanic Cloud distance and Gaia parallaxes, we calibrate the zero point of the period–luminosity relation to an error of 0.022 mag, which means that in the best case double-mode RR Lyr stars can anchor galaxy distances to an accuracy of 1.0%. For four globular clusters and two dwarf galaxies, we obtain distances using double-mode RR Lyr stars with a distance accuracy of 2–3% and 1–2%, respectively. With future telescopes such as the China Space Station Telescope and the Vera C. Rubin Observatory, double-mode RR Lyr stars will be established as an independent distance ladder in the near-field universe.

Astronomy & Astrophysics↗
DOE OSTI2024

Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Abstract Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.

Bowers, Charles F.↗
DOE OSTI2025

DESI 2024 III: baryon acoustic oscillations from galaxies and quasars

We present the DESI 2024 galaxy and quasar baryon acoustic oscillations (BAO) measurements using over 5.7 million unique galaxy and quasar redshifts in the range 0.1 < z < 2.1. Divided by tracer type, we utilize 300,017 galaxies from the magnitude-limited Bright Galaxy Survey with 0.1 < z < 0.4, 2,138,600 Luminous Red Galaxies with 0.4 < z < 1.1, 2,432,022 Emission Line Galaxies with 0.8 < z < 1.6, and 856,652 quasars with 0.8 < z < 2.1, over a ∼ 7,500 square degree footprint. The analysis was blinded at the catalog-level to avoid confirmation bias. All fiducial choices of the BAO fitting and reconstruction methodology, as well as the size of the systematic errors, were determined on the basis of the tests with mock catalogs and the blinded data catalogs. We present several improvements to the BAO analysis pipeline, including enhancing the BAO fitting and reconstruction methods in a more physically-motivated direction, and also present results using combinations of tracers. We employ a unified BAO analysis method across all tracers. We present a re-analysis of SDSS BOSS and eBOSS results applying the improved DESI methodology and find scatter consistent with the level of the quoted SDSS theoretical systematic uncertainties. With the total effective survey volume of ∼ 18 Gpc3, the combined precision of the BAO measurements across the six different redshift bins is ∼0.52%, marking a 1.2-fold improvement over the previous state-of-the-art results using only first-year data. We detect the BAO in all of these six redshift bins. The highest significance of BAO detection is 9.1σ at the effective redshift of 0.93, with a constraint of 0.86% placed on the BAO scale. We find that our observed BAO scales are systematically larger than the prediction of the Planck 2018-ΛCDM at z < 0.8. We translate the results into transverse comoving distance and radial Hubble distance measurements, which are used to constrain cosmological models in our companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Constraints on redshifts of blazars from extragalactic background light attenuation using Fermi - LAT data

ABSTRACT The extragalactic high-energy γ-ray sky is dominated by blazars, which are active galactic nuclei with their jets pointing towards us. Distance measurements are of fundamental importance yet for some of these sources are challenging because any spectral signature from the host galaxy may be outshone by the non-thermal emission from the jet. In this paper, we present a method to constrain redshifts for these sources that relies only on data from the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. This method takes advantage of the signatures that the pair-production interaction between photons with energies larger than approximately 10 GeV and the extragalactic background light leaves on γ-ray spectra. We find upper limits for the distances of 303 γ-ray blazars, classified as 157 BL Lacertae objects, 145 of uncertain class, and 1 flat-spectrum-radio quasar, whose redshifts are otherwise unknown. These derivations can be useful for planning observations with imaging atmospheric Cherenkov telescopes and also for testing theories of supermassive black hole evolution. Our results are applied to estimate the detectability of these blazars with the future Cherenkov Telescope Array, finding that at least 21 of them could be studied in a reasonable exposure of 20 h.

Astronomy & Astrophysics↗
DOE OSTI2020

Orbital Motion, Variability, and Masses in the T Tauri Triple System

We present results from adaptive optics imaging of the T Tauri triple system obtained at the Keck and Gemini Observatories in 2015−2019. We fit the orbital motion of T Tau Sb relative to Sa and model the astrometric motion of their center of mass relative to T Tau N. Using the distance measured by Gaia, we derived dynamical masses of M{sub Sa}=2.05±0.14 M {sub ⊙} and M {sub Sb} = 0.43 ± 0.06 M{sub ⊙}. The precision in the masses is expected to improve with continued observations that map the motion through a complete orbital period; this is particularly important as the system approaches periastron passage in 2023. Based on published properties and recent evolutionary tracks, we estimate a mass of ∼2 M {sub ⊙} for T Tau N, suggesting that T Tau N is similar in mass to T Tau Sa. Narrowband infrared photometry shows that T Tau N remained relatively constant between late 2017 and early 2019 with an average value of K = 5.54 ± 0.07 mag. Using T Tau N to calibrate relative flux measurements since 2015, we found that T Tau Sa varied dramatically between 7.0 and 8.8 mag in the K band over timescales of a few months, while T Tau Sb faded steadily from 8.5 to 11.1 mag in the K band. Over the 27 yr orbital period of the T Tau S binary, both components have shown 3–4 mag of variability in the K band, relative to T Tau N.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Probing Molecular Packing of Amorphous Pharmaceutical Solids Using X-ray Atomic Pair Distribution Function and Solid-State NMR

The structural investigation of amorphous pharmaceuticals is of paramount importance in comprehending their physicochemical stability. However, it has remained a relatively underexplored realm primarily due to the limited availability of high-resolution analytical tools. Here in this study, we utilized the combined power of X-ray pair distribution functions (PDFs) and solid-state nuclear magnetic resonance (ssNMR) techniques to probe the molecular packing of amorphous posaconazole and its amorphous solid dispersion at the molecular level. Leveraging synchrotron X-ray PDF data and employing the empirical potential structure refinement (EPSR) methodology, we unraveled the existence of a rigid conformation and discerned short-range intermolecular C–F contacts within amorphous posaconazole. Encouragingly, our ssNMR 19 F– 13 C distance measurements offered corroborative evidence supporting these findings. Furthermore, employing principal component analysis on the X-ray PDF and ssNMR data sets enabled us to gain invaluable insights into the chemical nature of the intermolecular interactions governing the drug–polymer interplay. These outcomes not only furnish crucial structural insights facilitating the comprehension of the underlying mechanisms governing the physicochemical stability but also underscore the efficacy of synergistically harnessing X-ray PDF and ssNMR techniques, complemented by robust modeling strategies, to achieve a high-resolution exploration of amorphous structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantum annealing algorithms for Boolean tensor networks

Abstract Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., $$\{0, 1\}$$ { 0 , 1 } ) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor’s with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Are the Brønsted acid sites in amorphous silica-alumina bridging?

Competing models exist to explain the differences in the activity of zeolites and amorphous silica–aluminas. Some postulate that silica–alumina contains dilute zeolitic bridging acid sites, while others favor a pseudo-bridging silanol model. Here, we employed a selective isotope labeling strategy to assess the existence of Si–O(H)–Al bonds using NMR-based distance measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ultra-deep imaging of NGC 1052-DF2 and NGC 1052-DF4 to unravel their origins

A number of scenarios have been proposed to explain the low velocity dispersion (and hence possible absence of dark matter) of the low surface brightness galaxies NGC 1052-DF2 and NGC 1052-DF4. Most of the proposed mechanisms are based on the removal of dark matter via the interaction of these galaxies with other objects. A common feature of these processes is the prediction of very faint tidal tails, which should be revealed by deep imaging (μ g > 30 mag arcsec −2 ). Using ultra-deep images obtained with the Gemini telescopes, about 1 mag deeper than previously published data, we analyzed the possible presence of tidal tails in both galaxies. We confirm the presence of tidal tails in NGC 1052-DF4, but see no evidence for tidal effects in NGC 1052-DF2, down to surface brightnesses ofμ g = 30.9 mag arcsec −2 . We therefore conclude that while the absence of dark matter in NGC 1052-DF4 could be attributed to the removal of dark matter by gravitational interactions, in the case of NGC 1052-DF2 this explanation seems less plausible, and therefore other possibilities such as an incorrect distance measurement or that the system may be rotating could alleviate the dark matter problem.

Astronomy & Astrophysics↗
DOE OSTI2020

Elucidating plasma dynamics in Hasegawa–Wakatani turbulence by information geometry

In this work, the impact of adiabatic electrons on drift-wave turbulence, modeled by the Hasegawa–Wakatani equations, is studied using information length. Information length is a novel theoretical method for measuring distances between statistical states represented by different probability distribution functions (PDFs) along the path of a system and represents the total number of statistically different states that a system evolves through in time. Specifically, the time-dependent PDFs of turbulent fluctuations for a given adiabatic index $A$ are computed. The changes in fluctuation statistics are then quantified in time by using information length. The numerical results provide time traces exhibiting intermittent plasma dynamics, and such behavior is identified by a rapid change in the information length. The effects of $A$ are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Computational optimal transport for molecular spectra: The semi-discrete case

Comparing a discrete molecular spectrum to a continuous molecular spectrum in a quantitative manner is a challenging problem, for example, when attempting to fit a theoretical stick spectrum to a continuous spectrum. In this paper, the use of computational optimal transport is investigated for such a problem. In the optimal transport literature, the comparison of a discrete and a continuous spectrum is referred to as semi-discrete optimal transport and is a situation where a metric such as least-squares may be difficult to define except under special conditions. The merits of an optimal transport approach for this problem are investigated using the transport distance defined for the semi-discrete case. A tutorial on semi-discrete optimal transport for molecular spectra is included in this paper, and several well-chosen synthetic spectra are investigated to demonstrate the utility of computational optimal transport for the semi-discrete case. Among several types of investigations, we include calculations showing how the frequency resolution of the continuous spectrum affects the transport distance between a discrete and a continuous spectrum. We also use the transport distance to measure the distance between a continuous experimental electronic absorption spectrum of SO 2 and a theoretical stick spectrum for the same system. The comparison of the theoretical and experimental SO 2 spectra also allows us to suggest a theoretical value for the band origin that is closer to the observed band origin than previous theoretical values.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

HOD-dependent systematics for luminous red galaxies in the DESI 2024 BAO analysis

In this paper, we present the estimation of systematics related to the halo occupation distribution (HOD) modeling in the baryon acoustic oscillations (BAO) distance measurement of the Dark Energy Spectroscopic Instrument (DESI) 2024 analysis. This paper focuses on the study of HOD systematics for luminous red galaxies (LRG). We consider three different HOD models for LRGs, including the base 5-parameter vanilla model and two extensions to it, that we refer to as baseline and extended models. The baseline model is described by the 5 vanilla HOD parameters, an incompleteness factor and a velocity bias parameter, whereas the extended one also includes a galaxy assembly bias and a satellite profile parameter. We utilize the 25 dark matter simulations available in the AbacusSummit simulation suite at z=0.8 and generate mock catalogs for our different HOD models. To test the impact of the HOD modeling in the position of the BAO peak, we run BAO fits for all these sets of simulations and compare the best-fit BAO-scaling parameters α iso and α AP between every pair of HOD models. Furthermore, we do this for both Fourier and configuration spaces independently, using post-reconstruction measurements. We find a 3.3σ detection of HOD systematic for α AP in configuration space with an amplitude of 0.19%. For the other cases, we did not find a 3σ detection, and we decided to compute a conservative estimation of the systematic using the ensemble of shifts between all pairs of HOD models. By doing this, we quote a systematic with an amplitude of 0.07% in α iso for both Fourier and configuration spaces; and of 0.09% in α AP for Fourier space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗