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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

The most luminous z ∼ 9-10 galaxy candidates yet found: The luminosity function, cosmic star-formation rate, and the first mass density estimate at 500 Myr

We present the discovery of four surprisingly bright (H {sub 160} ∼ 26-27 mag AB) galaxy candidates at z ∼ 9-10 in the complete HST CANDELS WFC3/IR GOODS-N imaging data, doubling the number of z ∼ 10 galaxy candidates that are known, just ∼500 Myr after the big bang. Two similarly bright sources are also detected in a reanalysis of the GOODS-S data set. Three of the four galaxies in GOODS-N are significantly detected at 4.5σ-6.2σ in the very deep Spitzer/IRAC 4.5 μm data, as is one of the GOODS-S candidates. Furthermore, the brightest of our candidates (at z = 10.2 ± 0.4) is robustly detected also at 3.6 μm (6.9σ), revealing a flat UV spectral energy distribution with a slope β = –2.0 ± 0.2, consistent with demonstrated trends with luminosity at high redshift. Thorough testing and use of grism data excludes known low-redshift contamination at high significance, including single emission-line sources, but as-yet unknown low redshift sources could provide an alternative solution given the surprising luminosity of these candidates. Finding such bright galaxies at z ∼ 9-10 suggests that the luminosity function for luminous galaxies might evolve in a complex way at z > 8. The cosmic star formation rate density still shows, however, an order-of-magnitude increase from z ∼ 10 to z ∼ 8 since the dominant contribution comes from low-luminosity sources. Based on the IRAC detections, we derive galaxy stellar masses at z ∼ 10, finding that these luminous objects are typically 10{sup 9} M {sub ☉}. This allows for a first estimate of the cosmic stellar mass density at z ∼ 10 resulting in log{sub 10} ρ{sub ∗}=4.7{sub −0.8}{sup +0.5} M {sub ☉} Mpc{sup –3} for galaxies brighter than M {sub UV} ∼ –18. The remarkable brightness, and hence luminosity, of these z ∼ 9-10 candidates will enable deep spectroscopy to determine their redshift and nature, and highlights the opportunity for the James Webb Space Telescope to map the buildup of galaxies at redshifts much earlier than z ∼ 10.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Concrete density estimation by rebound hammer method

Concrete is the most common and cheap material for radiation shielding. Compressive strength is the main parameter checked for determining concrete quality. However, for shielding purposes density is the parameter that needs to be considered. X- and -gamma radiations are effectively absorbed by a material with high atomic number and high density such as concrete. The high strength normally implies to higher density in concrete but this is not always true. This paper explains and discusses the correlation between rebound hammer testing and density for concrete containing hematite aggregates. A comparison is also made with normal concrete i.e. concrete containing crushed granite.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2011

TEMPERATURE AND DENSITY ESTIMATES OF EXTREME-ULTRAVIOLET FLARE RIBBONS DERIVED FROM TRACE DIFFRACTION PATTERNS

We report on Transition Region And Coronal Explorer 171 A observations of the GOES X20 class flare on 2001 April 2 that shows EUV flare ribbons with intense diffraction patterns. Between the 11th to 14th order, the diffraction patterns of the compact flare ribbon are dispersed into two sources. The two sources are identified as emission from the Fe IX line at 171.1 A and the combined emission from Fe X lines at 174.5, 175.3, and 177.2 A. The prominent emission of the Fe IX line indicates that the EUV-emitting ribbon has a strong temperature component near the lower end of the 171 A temperature response ({approx}0.6-1.5 MK). Fitting the observation with an isothermal model, the derived temperature is around 0.65 MK. However, the low sensitivity of the 171 A filter to high-temperature plasma does not provide estimates of the emission measure for temperatures above {approx}1.5 MK. Using the derived temperature of 0.65 MK, the observed 171 A flux gives a density of the EUV ribbon of 3 x 10{sup 11} cm{sup -3}. This density is much lower than the density of the hard X-ray producing region ({approx}10{sup 13} to 10{sup 14} cm{sup -3}) suggesting that the EUV sources, though closely related spatially, lie at higher altitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

mBEEF-vdW: Robust fitting of error estimation density functionals

Here, we propose a general-purpose semilocal/nonlocal exchange-correlation functional approximation, named mBEEF-vdW. The exchange is a meta generalized gradient approximation, and the correlation is a semilocal and nonlocal mixture, with the Rutgers-Chalmers approximation for van der Waals (vdW) forces. The functional is fitted within the Bayesian error estimation functional (BEEF) framework. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function, reducing the sensitivity to outliers in the datasets. To more reliably determine the optimal model complexity, we furthermore introduce a generalization of the bootstrap 0.632 estimator with hierarchical bootstrap sampling and geometric mean estimator over the training datasets. Using this estimator, we show that the robust loss function leads to a 10% improvement in the estimated prediction error over the previously used least-squares loss function. The mBEEF-vdW functional is benchmarked against popular density functional approximations over a wide range of datasets relevant for heterogeneous catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in gpaw and other density functional theory codes through Libxc, version 3.0.0.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparative analysis of plasticity-based GND density estimation methods in crystal plasticity finite element models

In crystal plasticity finite element (CPFE) simulations, accurately quantifying geometrically necessary dislocations (GNDs) is critical for capturing strain gradients in polycrystals. We compare different methods for quantifying GNDs, all of which originate from the Nye tensor, which is computed as the curl of the plastic deformation gradient. The projection technique directly decomposes the Nye tensor onto individual screw and edge dislocation components to compute GNDs. This approach requires converting a nine-component Nye tensor into densities for a larger number of dislocation systems, a fundamentally underdetermined (non-unique) process, which is resolved using L2 minimization. In contrast, when employing CPFE analysis, one could directly compute dislocation densities on each slip system using shear gradients. Projection and slip gradient methods are compared with respect to their prediction of GNDs with changing grain size, strain, and grain neighborhoods, including multigrain junctions. Although these techniques match analytical GND densities for single slip, single crystal deformation, and are consistent with anticipated overall GND trends, we find that the GND densities from projection techniques are significantly lower than those predicted from CPFE-based slip gradients in polycrystals. A suggested improvement of only using the active dislocation systems in the projection technique almost entirely resolved this mismatch.

Crystal plasticity↗
DOE OSTI2014

RADIATION PRESSURE DETECTION AND DENSITY ESTIMATE FOR 2011 MD

We present our astrometric observations of the small near-Earth object 2011 MD (H ∼ 28.0), obtained after its very close fly-by to Earth in 2011 June. Our set of observations extends the observational arc to 73 days, and, together with the published astrometry obtained around the Earth fly-by, allows a direct detection of the effect of radiation pressure on the object, with a confidence of 5σ. The detection can be used to put constraints on the density of the object, pointing to either an unexpectedly low value of ρ=(640±330)kg m{sup −3} (68% confidence interval) if we assume a typical probability distribution for the unknown albedo, or to an unusually high reflectivity of its surface. This result may have important implications both in terms of impact hazard from small objects and in light of a possible retrieval of this target.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

An efficient computational framework for charge density estimation in twisted bilayer graphene

Electronic properties such as band structure and Fermi velocity in low-angle twisted bilayer graphene (TBG) are intrinsically dependent on the atomic structure. Rigid rotation between individual graphene layers provides an approximate description of the bilayer symmetry. Upon relaxation, in-plane displacement of the atoms in low angle TBG causes a change in the symmetry through the enlargement of the AB stacking regions and the reduction in size of AA and SP stacking regions. However, the effect of this in-plane relaxation on the charge density remains unexplored, because the necessary electronic structure calculations of such large supercells of low twist angle TBG are computationally infeasible. Therefore, we develop a computationally efficient framework that enables the exploration of the charge density symmetry of the low twist angle TBG. This framework is based on the Fourier representation of the charge density which presents high intensity Bragg peaks. Here we find that with the decrease of twist angle, low intensity satellite peaks also become apparent. Our framework incorporates these satellite peaks which reveals transformation of symmetry in the charge density distribution from high to low twist angle TBG. One striking outcome is the demonstration of the electron localization in the AA region of low twist angle TBG. Our framework helps to explain the effect of the atomistic relaxation on the charge density distribution and thus, it provides information about exotic electronic properties of low twist angle TBG at a low computational expense.

36 MATERIALS SCIENCE↗
NASA NTRS2019

A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks— and more generally the autoencoder systems—should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations

Smith, Aaron↗
DOE OSTI2023

Fast and efficient identification of anomalous galaxy spectra with neural density estimation

ABSTRACT Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the probabilistic auto-encoder, to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae, and overlapping objects. Conditional modelling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available.

Böhm, Vanessa↗
DOE OSTI2021

Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]

The ChemCam instrument of Curiosity uses laser-induced breakdown spectroscopy (LIBS). It fires a laser at target and vaporizes rock surfaces, creating a plasma. Three spectrographs divide the plasma light into wavelengths for chemical analysis: ultraviolet, violet, and visible near-infrared. Regression methods (SVR, PCR, CNN) have been employed for calibration (prediction of the elemental composition of samples); however, labeled ChemCam samples are limited. Here, we focus on unsupervised learning and employ generative models from ChemCam analysis. Further, we use labels (supervised) in combination to the generative model to compute uncertainties related to predictions. We report generative modeling can be successfully applied to model real-world data. Normalizing flow models can be efficiently constructed on latent spaces for fast downstream inference. Unsupervised and supervised learning can be combined to form an uncertainty quantification framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗