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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
NASA NTRS2006

Deep Broad-Band Infrared Nulling Using A Single-Mode Fiber Beam Combiner and Baseline Rotation

The basic advantage of single-mode fibers for deep nulling applications resides in their spatial filtering ability, and has now long been known. However, and as suggested more recently, a single-mode fiber can also be used for direct coherent recombination of spatially separated beams, i.e. in a 'multi-axial' nulling scheme. After the first successful demonstration of deep (<2e-6) visible LASER nulls using this technique (Haguenauer & Serabyn, Applied Optics 2006), we decided to work on an infrared extension for ground based astronomical observations, e.g. using two or more off-axis sub-apertures of a large ground based telescope. In preparation for such a system, we built and tested a laboratory infrared fiber nuller working in a wavelength regime where atmospheric turbulence can be efficiently corrected, over a pass band (approx.1.5 to 1.8 micron) broad enough to provide reasonable sensitivity. In addition, since no snapshot images are readily accessible with a (single) fiber nuller, we also tested baseline rotation as an approach to detect off-axis companions while keeping a central null. This modulation technique is identical to the baseline rotation envisioned for the TPF-I space mission. Within this context, we report here on early laboratory results showing deep stable broad-band dual polarization infrared nulls <5e-4 (currently limited by detector noise), and visible LASER nulls better than 3e-4 over a 360 degree rotation of the baseline. While further work will take place in the laboratory to achieve deeper stable broad-band nulls and test off-axis sources detection through rotation, the emphasis will be put on bringing such a system to a telescope as soon as possible. Detection capability at the 500:1 contrast ratio in the K band (~2.2 microns) seem readily accessible within 50-100 mas of the optical axis, even with a first generation system mounted on a >5m AO equipped telescope such as the Palomar Hale 200 inch, the Keck, Subaru or Gemini telescopes.

single mode fibers↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Elevated ionizing photon production efficiency in faint high-equivalent-width Lyman-α emitters

ABSTRACT While low-luminosity galaxies dominate number counts at all redshifts, their contribution to cosmic reionization is poorly understood due to a lack of knowledge of their physical properties. We isolate a sample of 35 z ≈ 4–5 continuum-faint Lyman-α emitters from deep VLT/MUSE spectroscopy and directly measure their H α emission using stacked Spitzer/IRAC Ch. 1 photometry. Based on Hubble Space Telescope imaging, we determine that the average UV continuum magnitude is fainter than −16 (≈ 0.01 L⋆), implying a median Lyman-α equivalent width of 259 Å. By combining the H α measurement with the UV magnitude, we determine the ionizing photon production efficiency, ξion, a first for such faint galaxies. The measurement of log10 (ξion [Hz erg−1]) = 26.28 ($^{+0.28}_{-0.40}$) is in excess of literature measurements of both continuum- and emission line-selected samples, implying a more efficient production of ionizing photons in these lower luminosity, Lyman-α-selected systems. We conclude that this elevated efficiency can be explained by stellar populations with metallicities between 4 × 10−4 and 0.008, with light-weighted ages less than 3 Myr.

Maseda, Michael V.↗
NASA NTRS1989

Determination of Earth orientation using the Global Positioning System

Modern spacecraft tracking and navigation require highly accurate Earth-orientation parameters. For near-real-time applications, errors in these quantities and their extrapolated values are a significant error source. A globally distributed network of high-precision receivers observing the full Global Positioning System (GPS) configuration of 18 or more satellites may be an efficient and economical method for the rapid determination of short-term variations in Earth orientation. A covariance analysis using the JPL Orbit Analysis and Simulation Software (OASIS) was performed to evaluate the errors associated with GPS measurements of Earth orientation. These GPS measurements appear to be highly competitive with those from other techniques and can potentially yield frequent and reliable centimeter-level Earth-orientation information while simultaneously allowing the oversubscribed Deep Space Network (DSN) antennas to be used more for direct project support.

Freedman, A. P.↗
DOE OSTI2022

Serendipitous Discovery of Three Millisecond Pulsars with the GMRT in Fermi-directed Survey and Follow-up Radio Timing

We report the discovery of three millisecond pulsars (MSPs): PSRs J1120–3618, J1646–2142, and J1828+0625 with the Giant Metrewave Radio Telescope (GMRT) at a frequency of 322 MHz using a 32 MHz observing bandwidth. These sources were discovered serendipitously while conducting the deep observations to search for millisecond radio pulsations in the directions of unidentified Fermi Large Area Telescope (LAT) γ-ray sources. We also present phase coherent timing models for these MSPs using ~5 yr of observations with the GMRT. PSR J1120–3618 has a 5.5 ms spin period and is in a binary system with an orbital period of 5.6 days and minimum companion mass of 0.18 M ⊙ , PSR J1646–2142 is an isolated object with a spin period of 5.8 ms, and PSR J1828+0625 has a spin period of 3.6 ms and is in a binary system with an orbital period of 77.9 days and minimum companion mass of 0.27 M ⊙ . The two binaries have very low orbital eccentricities, in agreement with expectations for MSP-helium white dwarf systems. Using the GMRT 607 MHz receivers having a 32 MHz bandwidth, we have also detected PSR J1646–2142 and PSR J1828+0625, but not PSR J1120–3618. PSR J1646–2142 has a wide profile, with significant evolution between 322 and 607 MHz, whereas PSR J1120–3618 exhibits a single peaked profile at 322 MHz and PSR J1828+0625 exhibits a single peaked profile at both the observing frequencies. These MSPs do not have γ-ray counterparts, indicating that these are not associated with the target Fermi LAT pointing emphasizing the significance of deep blind searches for MSPs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Sidereal variations deep underground in Tasmania

Data from the deep underground vertically directed muon telescopes at Poatina, Tasmania, have been used since 1972 for a number of investigations, including the daily intensity variations, atmospheric influences, and checking for possible effects due to the interplanetary magnetic field. These telescopes have a total sensitive area of only 3 square meters, with the result that the counting rate is low (about 1680 events per hour) and the statistical errors on the results are rather large. Consequently, it was decided several years ago to construct larger detectors for this station. The first of these telescopes has been in operation for two complete years, and the results from it are presented. Results from the new, more stable equipment at Poatina appear to confirm the existence of a first harmonic in the daily variations in sidereal time reported earlier, and are consistent with small or non-existent first harmonics in solar and anti-sidereal time. All the second harmonics appear to be small, if not zero at these energies.

Humble, J. E.↗
NASA NTRS1993

Calculations on the competition between association and reaction for C3H(+) + H2

The ab initio results presently obtained for the potential energy surface of competing associative and reactive ion-molecule processes of the reactants H2 and C3H(+) show that the linear ion is able to directly access the deep potential well of the propargyl ion H2CCCH(+). Using the minimum energy potential pathway and properties of the stationary point structures determined via the ab initio methods, calculation results are obtained for both the association-rate coefficient for C3H3(+) production as a function of density, and the branching ratio between the propargyl and cyclic structures of the ion.

Maluendes, Sergio A.↗
NASA NTRS2001

DSMS science operations concept

The Deep Space Mission System (DSMS) Science Operations Concept describes the vision for enabling the use of the DSMS, particularly the Deep Space Network (DSN) for direct science observations in the areas of radio astronomy, planetary radar, radio science and VLBI.

deep space science↗
NASA NTRSDate not supplied

SafeDNN: Understanding and Verifying Neural Networks

The SafeDNN project at NASA Ames explores analysis techniques and tools to ensure that systems that use Deep Neural Networks (DNN) are safe, robust and interpretable. Research directions we are pursuing include: symbolic execution for DNN analysis, label-guided clustering to automatically identify input regions that are robust, parallel and compositional approaches to improve formal SMT-based verification, property inference and automated program repair for DNNs, adversarial training and detection, probabilistic reasoning for DNNs. In this talk I will highlight some of the research advances from SafeDNN, that were already published.

Corina Pasareanu↗
DOE OSTI2021

Stress analysis using direct-S wavelets produced by a vertical vibrator

We have determined how to extract the azimuth of maximum horizontal stress (SHmax) in deep rocks by doing a simple 360° mathematical rotation of a downgoing direct-S wavelet generated at the baseplate of a surface-based vertical vibrator. We worked with direct-S wavelets that travel through stressed rocks to a deep horizontal vertical seismic profile (VSP) geophone. We find that the azimuth where a polarity reversal occurs in mathematical rotations of this downgoing direct-S wavelet defines the azimuth of the SHmax in the overlying rocks. We tested this direct-S wavelet rotation method for determining the SHmax azimuth at a site in the Illinois Basin using legacy VSP data acquired in 2013. SHmax azimuths indicated by this simple wavelet-rotation method were determined when vertical vibrators were stationed at zero offset, at far offset, and at different azimuths around a VSP receiver well. These VSP-based SHmax azimuths agreed with the azimuth of the SHmax found by traditional minifrac tests in the VSP receiver well. This simple VSP data analysis procedure for detecting the azimuth of maximum horizontal stress has never, to our knowledge, been reported or discussed in the geophysical literature. This technical finding should be of interest to the worldwide geophysical community, especially to people who need to monitor how stress fields shift when fluids are injected into, or extracted from, deep porous reservoirs.

Geochemistry & Geophysics↗
DOE OSTI2021

Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_\mathrm f$, and the frequency and damping time of the ringdown oscillations of the fundamental $\ell = m = 2$ bar mode, $(\omega_\mathrm R, \omega_\mathrm I)$. Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1,m_2, a_\mathrm f, \omega_\mathrm R, \omega_\mathrm I)$ of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. Furthermore, this neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Navigation requirements for advanced deep space missions.

Study of the navigation system requirements and capabilities for potential advanced deep space missions of the 1978 to 1990 era. Following a review of these potential missions, the mission-by-mission assessments of the critical navigation system requirements for a mission set selected are presented. The requirements are related to subsystem research and development implications and are used to formulate recommendations for future developments in navigation systems. Programmatic directions required for deep space navigation are presented.

Friedman, L. D.↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Anisotropic jet broadening and jet shape

In this paper, we explore the use of jet substructure as a way of probing phenomena which break the isotropic behavior of jets, such as jet propagation through an anisotropically flowing quark-gluon plasma or spin correlations. We introduce two novel observables for this purpose: the azimuthal-dependent jet broadening and the azimuthal-dependent jet shape, which generalize the traditional isotropic substructure studies. Using Soft-Collinear Effective Theory, we explicitly calculate the jet functions associated with these observables with a standard jet axis and with a Winner-Take-All jet axis in both the resummed and fixed order limits. While our analysis first and foremost establishes the formalism for the azimuthal-dependent jet substructure, it also brings to light new results for jet substructure in the azimuthally integrated case, such as the semi-inclusive jet function and the exclusive jet shape for the Winner-Take-All axis, and the jet broadening in the fixed order region. As an illustrative example for the new formalism we demonstrate that the azimuthal-dependent jet broadening can be used as a direct probe of the transversity parton distribution function in deep inelastic scattering.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Applying Machine Learning to the Classification of DC-DC Converters. NA-22 Final Report

Tools are now available that enable measurement electromagnetic radiation (EMR) from active electronics in an item. This radiation may be intended WIFI or cellular network links, for example or unintended such as the switching noise generated by DC-to-DC converters. It would be extremely valuable to have the capability to discriminate between the low-voltage DC-to-DC converters or other digital noise prevalent in most modern electronics, versus the high-voltage DC-to-DC converters used in utility firesets. Previous work performed under a Sandia Laboratory Directed Research and Development (LDRD) project on Charge State Detection using a deep neural network has been continued in this effort. A state-of-the-art supervised machine learning algorithm has not only been extended to discriminate between low and high voltage converters but has been validated in determining a converters make and model.

42 ENGINEERING↗
NASA NTRS1984

Deep space communication - Past, present, and future

This paper reviews the progress made in deep space communication from its beginnings until now, describes the development and applications of NASA's Deep Space Network, and indicates directions for the future. Limiting factors in deep space communication are examined using the upcoming Voyager encounter with Uranus, centered on the downlink telemetry from spacecraft to earth, as an example. A link calculation for Voyager at Uranus over Australia is exhibited. Seven basic deep space communication functions are discussed, and technical aspects of spacecraft communication equipment, ground antennas, and ground electronics and processing are considered.

Posner, E. C.↗
DOE OSTI2023

Identification and characterization of deep nitrogen acceptors in β-Ga 2 O 3 using defect spectroscopies

The ability to achieve highly resistive beta-phase gallium oxide (β-Ga 2 O 3 ) layers and substrates is critical for β-Ga 2 O 3 high voltage and RF devices. To date, the most common approach involves doping with iron (Fe), which generates a moderately deep acceptor-like defect state located at E C -0.8 eV in the β-Ga 2 O 3 bandgap. Recently, there has been growing interest in alternative acceptors, such as magnesium (Mg) and nitrogen (N), due to their predicted deeper energy levels, which could avoid inadvertent charge modulation during device operation. In this work, a systematic study that makes direct correlations between the introduction of N using ion implantation and the observation of a newly observed deep level at E C -2.9 eV detected by deep-level optical spectroscopy (DLOS) is presented. The concentration of this state displayed a monotonic dependence with N concentration over a range of implant conditions, as confirmed by secondary ion mass spectrometry (SIMS). With a near 1:1 match in absolute N and E C -2.9 eV trap concentrations from SIMS and DLOS, respectively, which also matched the measured removal of free electrons from capacitance-voltage studies, this indicates that N contributes a very efficiently incorporated compensating defect. Density functional theory calculations confirm the assignment of this state to be an N (0/-1) acceptor with a configuration of N occupying the oxygen site III [N O(III) ]. The near ideal efficiency for this state to compensate free electrons and its location toward the midgap region of the β-Ga 2 O 3 bandgap demonstrates the potential of N doping as a promising approach for producing semi-insulating β-Ga 2 O 3 .

36 MATERIALS SCIENCE↗