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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Planck intermediate results: XXXIX. The Planck list of high-redshift source candidates

The Planck mission, thanks to its large frequency range and all-sky coverage, has a unique potential for systematically detecting the brightest, and rarest, submillimetre sources on the sky, including distant objects in the high-redshift Universe traced by their dust emission. In this paper, a novel method, based on a component-separation procedure using a combination of Planck and IRAS data, has been validated and characterized on numerous simulations, and applied to select the most luminous cold submillimetre sources with spectral energy distributions peaking between 353 and 857 GHz at 5' resolution. A total of 2151 Planck high-z source candidates (the PHZ) have been detected in the cleanest 26% of the sky, with flux density at 545 GHz above 500 mJy. Embedded in the cosmic infrared background close to the confusion limit, these high-z candidates exhibit colder colours than their surroundings, consistent with redshifts z > 2, assuming a dust temperature of T xgal = 35 K and a spectral index of β xgal = 1.5. Exhibiting extremely high luminosities, larger than 10 14 L ⊙ , the PHZ objects may be made of multiple galaxies or clumps at high redshift, as suggested by a first statistical analysis based on a comparison with number count models. Furthermore, first follow-up observations obtained from optical to submillimetre wavelengths, which can be found in companion papers, have confirmed that this list consists of two distinct populations. A small fraction (around 3%) of the sources have been identified as strongly gravitationally lensed star-forming galaxies at redshift 2 to 4, while the vast majority of the PHZ sources appear as overdensities of dusty star-forming galaxies, having colours consistent with being at z > 2, and may be considered as proto-cluster candidates. The PHZ provides an original sample, which is complementary to the Planck Sunyaev-Zeldovich Catalogue (PSZ2); by extending the population of virialized massive galaxy clusters detected below z < 1.5 through their SZ signal to a population of sources at z > 1.5, the PHZ may contain the progenitors of today’s clusters. Therefore the Planck list of high-redshift source candidates opens a new window on the study of the early stages of structure formation, particularly understanding the intensively star-forming phase at high-z.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Ge quantum dots encapsulated by AlAs grown by molecular beam epitaxy on GaAs without extended defects

We demonstrate nearly spherical, strain-free, self-assembled Ge quantum dots (QDs) fully encapsulated by AlAs, grown on (100) GaAs by molecular beam epitaxy. The QDs were formed without a wetting layer using a high temperature, in situ anneal. Subsequent AlAs overgrowth was free from anti-phase domains and threading dislocations in cross section transmission electron microscopy. The straddling band alignment for Ge in AlAs promises strong and tunable confinement for both electrons and holes. The reflection high-energy electron diffraction pattern changed from 2 × 3 to 2 × 5 with anneal, which can be explained by surface reconstructions based on the electron-counting model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

GALAXIES BEHIND THE GALACTIC PLANE: FIRST RESULTS AND PERSPECTIVES FROM THE VVV SURVEY

VISTA Variables in the Via Lactea (VVV) is an ESO variability survey that is performing observations in near-infrared bands (ZY JHK{sub s}) toward the Galactic bulge and part of the disk with the completeness limits at least 3 mag deeper than Two Micron All Sky Survey. In the present work, we searched in the VVV survey data for background galaxies near the Galactic plane using ZY JHK{sub s} photometry that covers 1.636 deg{sup 2}. We identified 204 new galaxy candidates by analyzing colors, sizes, and visual inspection of multi-band (ZY JHK{sub s}) images. The galaxy candidate colors were also compared with the predicted ones by star count models considering a more realistic extinction model at the same completeness limits observed by VVV. A comparison of the galaxy candidates with the expected one by Millennium simulations is also presented. Our results increase the number density of known galaxies behind the Milky Way by more than one order of magnitude. A catalog with galaxy properties including ellipticity, Petrosian radii, and ZY JHK{sub s} magnitudes is provided, as well as comparisons of the results with other surveys of galaxies toward the Galactic plane.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

On/Off State Classification of a Reactor Facility Using Gas Effluence Measurements

Inferring on/off operational state of a reactor facility using measurements from an independent monitoring system is critical to the assessment of its compliance to agreements. In this paper, we consider the problem of inferring on/off state of a reactor facility using measurements of Ar-41, Cs-138, and Xe-138 gas effluence types collected at the facility’s off-gas stack. We present classifiers based on thresholding measurements of individual effluence types, and then present fusers that combine their outputs or measurements. We present five fusers based on simple majority rule, Chow’s pattern recognition function, Fisher’s combined p-value statistic, physics-based Poisson radiation counts model, and correlation coefficient method. In addition, we also test five machine learning methods based on non-linear classifiers, which are available as R packages. Finally, our results show that: (i) these gas effluence measurements are effective in inferring on/off state of a reactor facility; for example, best fusers achieve ~97% detection at ~1% false alarm rate, and (ii) fusers that combine all effluence types based on physics-based models, correlation coefficient and Fisher’s method outperform simple majority rule, Chow’s fusers and machine learning methods, as well as when they are applied to individual and pairs of effluence types.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets

Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chain Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.Conclusions: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

A UNIFIED EMPIRICAL MODEL FOR INFRARED GALAXY COUNTS BASED ON THE OBSERVED PHYSICAL EVOLUTION OF DISTANT GALAXIES

We reproduce the mid-infrared to radio galaxy counts with a new empirical model based on our current understanding of the evolution of main-sequence (MS) and starburst (SB) galaxies. We rely on a simple spectral energy distribution (SED) library based on Herschel observations: a single SED for the MS and another one for SB, getting warmer with redshift. Our model is able to reproduce recent measurements of galaxy counts performed with Herschel, including counts per redshift slice. This agreement demonstrates the power of our 2-Star-Formation Modes (2SFM) decomposition in describing the statistical properties of infrared sources and their evolution with cosmic time. We discuss the relative contribution of MS and SB galaxies to the number counts at various wavelengths and flux densities. We also show that MS galaxies are responsible for a bump in the 1.4 GHz radio counts around 50 {mu}Jy. Material of the model (predictions, SED library, mock catalogs, etc.) is available online.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Supervised Gamma Process Poisson Factorization

This thesis develops the supervised gamma process Poisson factorization (S- GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels. S-GPPF is fully generative and nonparametric: document labels and count matrices are modeled under a uni ed probabilistic framework and the number of latent topics is controlled automatically via a gamma process prior. The framework provides for multi-class classification of documents using a generative max-margin classifier. Several recent data augmentation techniques are leveraged to provide for exact inference using a Gibbs sampling scheme. The first portion of this thesis reviews supervised topic modeling and several key mathematical devices used in the formulation of S-GPPF. The thesis then introduces the S-GPPF generative model and derives the conditional posterior distributions of the latent variables for posterior inference via Gibbs sampling. The S-GPPF is shown to exhibit state-of-the-art performance for joint topic modeling and document classification on a dataset of conference abstracts, beating out competing supervised topic models. The unique properties of S-GPPF along with its competitive performance make it a novel contribution to supervised topic modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Anomaly Detection in Dynamic Networks

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Spin Unrestricted Nonradiative Relaxation Dynamics of Cobalt-Doped Anatase Nanowire

Spin-resolved charge transfer dynamics at the interface of a Co(NH3)2-doped (001) anatase TiO2 nanowire and liquid water calculations based on density functional theory and density matrix formalism are considered. Three models with the same stoichiometry but different electronic structure are explored. While one model had no change to electron count and spin count (neutral model), the other two models were assigned a charge of 2+, one in a doublet and the other a quartet spin configuration. Co2+ is the most probable state for dopant in all models and Co acts as an electron acceptor. The optical absorption spectra show a rather unique pattern post 400 nm where the α and β absorptions happen independently at different frequency ranges. Essentially, the relaxation dynamics can be controlled as when an α electron is excited, there is a near zero probability of a β electron also undergoing an excitation and vice versa. The isolated exemption is between 400 and 650 nm in the neutral model. All the models absorb light in the visible range, while the electrons and holes have drastic differences in relaxation rates. The spatial charge separation occurs upon excitation and subsequent trapping, designating the considered system a prospect for electrochemical cell applications. (2+) models trap states more effectively slow down electron relaxation, making the charged models slightly better options for application compared to the neutral model.

Jensen, Stephanie J.↗
DOE OSTI2022

Towards a Counting Point Detector for Nanosecond Coherent X-ray Science

We present the technical realization of a high-speed hard X-ray single-photon counting-detection scheme based on a commercial avalanche silicon photodiode and high-speed oscilloscope. The development is motivated by the need to perform pulse-resolved photon-correlation and pump-probe studies at synchrotron sources with densely packed pulse patterns that result in high repetition rate pulses on the order of hundreds of MHz. Commissioning experiments are performed at the 1C PAL-KRISS beamline at PLS-II of South Korea operating at a burst mode maximum repetition rate of 500 MHz. In such a high count-rate measurement, detector dead-time can lead to a distortion of counting statistics. We are able to model the counting behavior of our detector under these conditions with a detector dead-time comparable to time between X-ray pulses, implying that nanosecond X-ray photon correlation spectroscopy should be possible at diffraction-limited light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning

Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. In conclusion, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Constraining thawing and freezing models with cluster number counts

Measurements of the cluster abundance as a function of mass and redshift provide an important cosmological test that probe not only the expansion rate but also the growth of perturbations. In this paper we adopt a scalar field scenario which admits both thawing and freezing solutions from an appropriate choice of the model parameters and derived all relevant expressions to calculate the mass function and the cluster number density. We discuss the ability of cluster observations to distinguish between these scalar field behaviors and the standard ΛCDM scenario by considering the eROSITA and SPT cluster surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System

In this work, we perform analysis of detection and counting of cars using a low-power IBM TrueNorth Neurosynaptic System. For our evaluation we looked at a publicly-available dataset that has overhead imagery of cars with context present in the image. The trained neural network for image analysis was deployed on the NS16e system using IBM's EEDN training framework. Through multiple experiments we identify the architectural bottlenecks present in TrueNorth system that does not let us deploy large neural network structures. Following these experiments we propose changes to CNN model to circumvent these architectural bottlenecks. The results of these evaluations have been compared with caffe-based implementations of standard neural networks that were deployed on a Titan-X GPU. Results showed that TrueNorth can detect cars from the dataset with 97.60% accuracy and can be used to accurately count the number of cars in the image with 69.04% accuracy. The car detection accuracy and car count (–/+ 2 error margin) accuracy are comparable to high-precision neural networks like AlexNet, GoogLeNet, and ResCeption, but show a manifold improvement in power consumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Millimeter-wave Point Sources from the 2500 Square Degree SPT-SZ Survey: Catalog and Population Statistics

In this work, we present a catalog of emissive point sources detected in the SPT-SZ survey, a contiguous 2530- square-degree area surveyed with the South Pole Telescope (SPT) from 2008 - 2011 in three bands centered at 95, 150, and 220GHz. The catalog contains 4845 sources measured at a significance of 4.5σ or greater in at least one band, corresponding to detections above approximately 9.8, 5.8, and 20.4 mJy in 95, 150, and 220 GHz, respectively. Spectral behavior in the SPT bands is used for source classification into two populations based on the underlying physical mechanisms of compact, emissive sources that are bright at millimeter wavelengths: synchrotron radiation from active galactic nuclei and thermal emission from dust. The latter population includes a component of high-redshift sources often referred to as submillimeter galaxies (SMGs). In the relatively bright flux ranges probed by the survey, these sources are expected to be magnified by strong gravitational lensing. The survey also contains sources consistent with protoclusters, groups of dusty galaxies at high redshift undergoing collapse. We cross-match the SPT-SZ catalog with external catalogs at radio, infrared, and X-ray wavelengths and identify available redshift information. The catalog splits into 3980 synchrotron-dominated and 865 dust-dominated sources and we determine a list of 506 SMGs. Ten sources in the catalog are identified as stars. We calculate number counts for the full catalog, and synchrotron and dusty components, using a bootstrap method and compare our measured counts with models. This paper represents the third and final catalog of point sources in the SPT-SZ survey.

79 ASTRONOMY AND ASTROPHYSICS↗