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DOE OSTI2015

AKARI OBSERVATION OF THE SUB-DEGREE SCALE FLUCTUATION OF THE NEAR-INFRARED BACKGROUND

We report spatial fluctuation analysis of the sky brightness in the near-infrared from observations toward the north ecliptic pole (NEP) by the AKARI at 2.4 and 3.2 μm. As a follow-up study of our previous work on the Monitor field of AKARI, we used NEP deep survey data, which covered a circular area of about 0.4 square degrees, in order to extend fluctuation analysis at angular scales up to 1000″. We found residual fluctuation over the estimated shot noise at larger angles than the angular scale of the Monitor field. The excess fluctuation of the NEP deep field smoothly connects with that of the Monitor field at angular scales of a few hundred arcseconds and extends without any significant variation to larger angular scales up to 1000″. By comparing excess fluctuations at two wavelengths, we confirm a blue spectral feature similar to the result of the Monitor field. We find that the result of this study is consistent with Spitzer Space Telescope observations at 3.6 μm. The origin of the excess fluctuation in the near-infrared background remains to be determined, but we could exclude zodiacal light, diffuse Galactic light, and unresolved faint galaxies at low redshift based on the comparison with mid- and far-infrared brightness, ground-based near-infrared images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

Here we discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co 20 Fe 61 B 19 specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe 23 B 6 as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Strong evidence that the galactic bulge is shining in gamma rays

There is growing evidence that the Galactic Center Excess identified in the Fermi-LAT gamma-ray data arises from a population of faint astrophysical sources. We provide compelling supporting evidence by showing that the morphology of the excess traces the stellar over-density of the Galactic bulge. By adopting a template of the bulge stars obtained from a triaxial 3D fit to the diffuse near-infrared emission, we show that it is detected at high significance. The significance deteriorates when either the position or the orientation of the template is artificially shifted, supporting the correlation of the gamma-ray data with the Galactic bulge. In deriving these results, we have used more sophisticated templates at low-latitudes for the Fermi bubbles compared to previous work and the three-dimensional Inverse Compton (IC) maps recently released by the GALPROP team. Our results provide strong constraints on Millisecond Pulsar (MSP) formation scenarios proposed to explain the excess. We find that an admixture formation scenario, in which some of the relevant binaries are primordial and the rest are formed dynamically, is preferred over a primordial-only formation scenario at 7.6σ confidence level. Our detailed morphological analysis also disfavors models of the disrupted globular clusters scenario that predict a spherically symmetric distribution of MSPs in the Galactic bulge. For the first time, we report evidence of a high energy tail in the nuclear bulge spectrum that could be the result of IC emission from electrons and positrons injected by a population of MSPs and star formation activity from the same site.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DeepSZ: identification of Sunyaev–Zel’dovich galaxy clusters using deep learning

Galaxy clusters identified via the Sunyaev–Zel’dovich (SZ) effect are a key ingredient in multiwavelength cluster cosmology. In this paper, we present and compare three methods of cluster identification: the standard matched filter (MF) method in SZ cluster finding, a convolutional neural networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cut-out images of the sky as input, identifying the cut-out as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with signal-to-noise-ratio below the MF detection threshold. However, the CNN tends to mis-classify cut-out whose clusters are located near the edge of the cut-out, which can be mitigated with staggered cut-out. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Synthetic light-cone catalogues of modern redshift and weak lensing surveys with abacussummit

ABSTRACT The joint analysis of different cosmological probes, such as galaxy clustering and weak lensing, can potentially yield invaluable insights into the nature of the primordial Universe, dark energy, and dark matter. However, the development of high-fidelity theoretical models is a necessary stepping stone. Here, we present public high-resolution weak lensing maps on the light-cone, generated using the N-body simulation suite abacussummit, and accompanying weak lensing mock catalogues, tuned to the Early Data Release small-scale clustering measurements of the Dark Energy Spectroscopic Instrument. Available in this release are maps of the cosmic shear, deflection angle, and convergence fields at source redshifts ranging from z = 0.15 to 2.45 as well as cosmic microwave background convergence maps for each of the 25 base-resolution simulations ($L_{\rm box} = 2000\, h^{-1}\, {\rm Mpc}$ and Npart = 69123) as well as for the two huge simulations ($L_{\rm box} = 7500\, h^{-1}\, {\rm Mpc}$ and Npart = 86403) at the fiducial abacussummit cosmology. The pixel resolution of each map is 0.21 arcmin, corresponding to a healpix Nside of 16 384. The sky coverage of the base simulations is an octant until z ≈ 0.8 (decreasing to about 1800 deg2 at z ≈ 2.4), whereas the huge simulations offer full-sky coverage until z ≈ 2.2. Mock lensing source catalogues are sampled matching the ensemble properties of the Kilo-Degree Survey, Dark Energy Survey, and Hyper Suprime-Cam data sets. The mock catalogues are validated against theoretical predictions for various clustering and lensing statistics, such as correlation multipoles, galaxy–shear, and shear–shear, showing excellent agreement. All products can be downloaded via a Globus endpoint (see Data Availability section).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Large Longitudinal Spin Alignment of Excited Projectiles in Intermediate Energy Inelastic Scattering

Here, we study the sequential breakup of E/A = 24.0 MeV 7 Li projectiles excited through inelastic interactions with C, Be, and Al target nuclei. For peripheral events that do not excite the target, we find very large spin alignment of the excited 7 Li projectiles longitudinal to the beam axis. This spin alignment is independent of the target used, and we propose a simple alignment mechanism that arises from an angular-momentum-excitation-energy mismatch. This mechanism is independent of the potential used for scattering and should be present in many scattering experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Erratum: Centrality-Dependent Modification of Jet-Production Rates in Deuteron-Gold Collisions at $\sqrt{𝑠_{𝑁⁢𝑁}}$ = 200 GeV [Phys. Rev. Lett. 116, 122301 (2016)]

We previously reported jet-production rates in 𝑝 + 𝑝 and 𝑑 + Au collisions at $\sqrt{𝑠_{𝑁⁢𝑁}}$ = 200 GeV recorded in 2008 with the PHENIX detector at the Relativistic Heavy Ion Collider. Subsequently, we found that electronic noise in the electromagnetic calorimeter was not completely removed by the tower-timing cuts used in the analysis. We have conducted an independent re-analysis of the data that eliminated noisy towers by removing them completely from the analysis. These cuts remove any contributions from instrumental noise, but reduce the overall acceptance for electromagnetic clusters by ≈16% and hence the overall statistical power of the data. Finally, this re-analysis found that instrumental noise in the electromagnetic calorimeter led to an artificial increase in the jet yield for both the 𝑝 + 𝑝 and 𝑑 + Au collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Quorum Quenching of Nitrobacter winogradskyi Suggests that Quorum Sensing Regulates Fluxes of Nitrogen Oxide(s) during Nitrification

ABSTRACT Quorum sensing (QS) is a widespread process in bacteria used to coordinate gene expression with cell density, diffusion dynamics, and spatial distribution through the production of diffusible chemical signals. To date, most studies on QS have focused on model bacteria that are amenable to genetic manipulation and capable of high growth rates, but many environmentally important bacteria have been overlooked. For example, representatives of proteobacteria that participate in nitrification, the aerobic oxidation of ammonia to nitrate via nitrite, produce QS signals called acyl-homoserine lactones (AHLs). Nitrification emits nitrogen oxide gases (NO, NO 2 , and N 2 O), which are potentially hazardous compounds that contribute to global warming. Despite considerable interest in nitrification, the purpose of QS in the physiology/ecology of nitrifying bacteria is poorly understood. Through a quorum quenching approach, we investigated the role of QS in a well-studied AHL-producing nitrite oxidizer, Nitrobacter winogradskyi . We added a recombinant AiiA lactonase to N. winogradskyi cultures to degrade AHLs to prevent their accumulation and to induce a QS-negative phenotype and then used mRNA sequencing (mRNA-Seq) to identify putative QS-controlled genes. Our transcriptome analysis showed that expression of nirK and nirK cluster genes ( ncgABC ) increased up to 19.9-fold under QS-proficient conditions (minus active lactonase). These data led to us to query if QS influenced nitrogen oxide gas fluxes in N. winogradskyi . Production and consumption of NO x increased and production of N 2 O decreased under QS-proficient conditions. Quorum quenching transcriptome approaches have broad potential to identify QS-controlled genes and phenotypes in organisms that are not genetically tractable. IMPORTANCE Bacterial cell-cell signaling, or quorum sensing (QS), is a method of bacterial communication and gene regulation that is well studied in bacteria. However, little is known about the purpose of QS in many environmentally important bacteria. Here, we demonstrate quorum quenching coupled with mRNA-Seq to identify QS-controlled genes and phenotypes in Nitrobacter winogradskyi , a nitrite-oxidizing bacterium. Nitrite oxidizers play an important role in the nitrogen cycle though their participation in nitrification, the aerobic oxidation of ammonia to nitrate via nitrite. Our quorum quenching approach revealed that QS influences production and consumption of environmentally important nitrogen oxide gases (NO, NO 2 , and N 2 O) in N. winogradskyi . This study demonstrated a novel technique for studying QS in difficult-to-work-with microorganisms and showed that nitrite oxidizers might also contribute to nitrification-dependent production of nitrogen oxide gases that contribute to global warming.

Mellbye, Brett L.↗
DOE OSTI2022

Real-time X-ray phase-contrast imaging using SPINNet—a speckle-based phase-contrast imaging neural network

X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantages in spatial resolution, phase sensitivity, and implementation flexibility compared with traditional methods. However, the computational cost associated with data inversion has hindered their wider adoption. By exploiting the power of deep learning, we developed a speckle-based phase-contrast imaging neural network (SPINNet) that significantly improves the imaging quality and boosts the phase retrieval speed by at least 2 orders of magnitude compared to existing methods. To achieve this performance, we combined SPINNet with a coded-mask-based technique, an enhanced version of the speckle-based method. Using this scheme, we demonstrate the simultaneous reconstruction of absorption and phase images on the order of 100 ms, where a traditional correlation-based analysis would take several minutes even with a cluster. In addition to significant improvement in speed, our experimental results show that the imaging and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods. Furthermore, we successfully demonstrate SPINNet application in x-ray optics metrology and 3D x-ray phase-contrast tomography. Our result shows that SPINNet could enable many applications requiring high-resolution and fast data acquisition and processing, such as in situ and in operando 2D and 3D phase-contrast imaging and real-time at-wavelength metrology and wavefront sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

The Hunt for Green Every April: Factors Affecting Fitness in Switchgrass

This grant funded work was undertaken to develop fundamental biological knowledge of the factors affecting the complex plant trait “fitness” in switchgrass (Panicum virgatum L.), a plant being developed as a biomass crop. Using a diverse range of latitudinally-adapted switchgrass plants, genomic, molecular and physiological studies were performed to track a number of different aspects of plant genetics and physiology over the course of the growing season. Work was performed on both genetically unrelated and genetically related plants. Plants were established in the field from seedlings raised in a greenhouse, or from clones present in other field nurseries. Field grown plants were used as the source of all tissues. The three objectives of this proposal were:(1) Transcript Profiling, Metabolomics, and C and N Partitioning and Recycling in Crowns and Rhizomes of Switchgrass over two growing seasons; (2) Gene Profiling During Regreening and Dormancy of Bulked Segregants; (3) Extent of Linkage Disequilibrium in Populations for Adaptation and Fitness Traits Being Developed for Central and Northern USA, that Show Significant Heterosis. Objective 1 results: Plants were labeled using 13 CO2 (a stable isotope) using an acrylic chamber constructed specifically for this purpose. Plants became labeled with 13 C and label decayed in aerial tissues over the course of the growing season. Varying amounts of 13 C were recovered in the rhizomes. These data are being analyzed. Plants were also labeled with 15 N-urea. Plants absorbed significant amounts of label that was remobilized to the growing shoots. N-dynamics would suggest that a portion of the 15 N absorbed into the crowns and rhizomes is sequestered below ground. Variable amounts of 15 N were translocated from the shoots to the roots over the course of the growing season. Polar metabolites extracted from a diverse array of rhizomes were analyzed using GCMS. Data indicated that there was a significant shift in metabolite pools over the course of the growing season, and differences in the levels of specific metabolites could be linked to the progression of dormancy. Several metabolites that accumulate in dormant rhizomes were identified. Some of these metabolites could be potentially linked to winter-survival of switchgrass. Extensive high-throughput sequencing was conducted on crown and rhizome samples collected from field grown plants. Initial work was performed on a Roche 454 system. All later work was performed on an Illumina sequencing-by-synthesis system. Some of these datasets have been published as peer-reviewed papers, other data are currently being analyzed and being readied for publication. Objective 2 results: Genetically related but phenotypically divergent plants from an octaploid switchgrass population were grown in a replicated field nursery. Rhizomes were harvested at four different times over the course of the growing season from plants with high winter survival and those with lower winter survival. RNA-Seq was performed on harvested materials. Initial analysis suggests that plants with lowered winter survival experience a greater level of cellular stress in dormant tissues. This aspect of plant function is being probed in greater depth. Objective 3 results: A total of 592 individual clones with three clonal replications in a randomized complete block design from each of five populations used in Objective 1 studies were rated for heading date in 2012 and 2014, green-up day of year in 2013, anthesis date in 2012, and yield in 2012, they were also subjected to NIR spectroscopy to derive cell wall composition estimates based on prior NIR calibrations. Plants were genotyped via a genotyping by sequencing (GBS) approach from reduced representation libraries constructed with adaptors that identified each individual. Libraries generated with the restriction enzyme PstI and called SNPs using Samtools after alignment to version 1.1 of the switchgrass genome sequence. A total of approximately 40,000 SNPs were found. These were then further filtered to eliminate markers with a minor allele frequency of < 0.05. The results of population analysis using STRUCTURE with expected population sizes or cluster numbers (K), clearly shows the hybrid composition of the KxS population and discriminated easily between upland (Summer) and lowland (Kanlow) populations. Under an assumption of 5 distinct populations there were detectable differences in allele frequencies between subpopulations within the three Kanlow populations particularly with respect to Kanlow EM and Kanlow base. We detected 110 SNPs with an allele frequency difference of ≥ 0.2 between Kanlow EM and Kanlow base populations, while 120 SNPs showed an allele frequency difference of ≥ 0.15 between Kanlow N1 and Kanlow base populations. These data are being readied for publication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integrating Concentrating Solar Power Technologies into the Hybrid Optimization and Performance Platform (HOPP)

As the world increases renewable energy deployment, there is an increasing interest in hybridizing various generation and storage technologies to maximize net benefit to the developer and/or off-taker. A particularly interesting combination of renewable technologies is concentrating solar power (CSP) with thermal energy storage (TES), photovoltaics (PV), and electrochemical battery energy storage (BESS). Due to the system complexity of CSP technology, it is difficult to evaluate the technological and financial performance of a CSP-PV hybrid system without detailed modeling of annual operations. To address this challenge, we have developed a modeling framework for evaluating the performance and financial viability of CSP systems hybridized with PV and battery technologies. This modeling effort incorporates CSP tower and trough systems into an existing modeling tool recently developed by NREL referred to as the Hybrid Optimization and Performance Platform (HOPP). This report outlines the modeling methodology as well as preliminary results from example case studies conducted using the model. The methodology describes: (i) the integration of CSP tower and troughs into HOPP using python interfaces to access System Advisor Model (SAM) underlining technology models, (ii) the mathematical formulation of the mixed integer linear program dispatch optimization model which optimizes operations of storage asset to either maximize system revenue or minimize operating cost while load following, (iii) the design analysis methods implemented within HOPP, and (iv) simulation clustering for the purposes of reducing computational expense. We exercise the model using a case study of a future scenario where we assume (i) CSP and PV technologies achieve the 2030 cost targets provided by the Solar Energy Technologies Office (SETO), (ii) battery costs reduce to the 2030 mid cost projection presented by NREL. Lastly, (iii) electricity prices for southern California in 2030 are provided by NREL's Cambium database, and (iv) a capacity payment of $150/kW-yr based on the system capacity factor during the to 100 net-load hours.

14 SOLAR ENERGY↗
DOE OSTI2024

Collaborative Research: Vlasov-Maxwell Simulations to Resolve Electron Heating and Dissipation, in Quasi-Perpendicular Shocks (Final Report)

Collisionless shocks are a grand challenge problem in plasma physics and have been the subject of study for more than six decades. Shocks are ubiquitous phenomena in the universe and are responsible for transforming high energy and momentum flows into thermal energy and energetic particles. Understanding how shocks operate is of primary importance to understand the sun-earth coupling, protecting manned missions and spacecrafts from high energy particles, inertial confinement fusion, and radiation observed from astrophysical plasmas, such as supernova remnants and astrophysical jets. A major unanswered question on this frontier is, how does a collisionless plasma transform flow energy and momentum into electron thermal energy? Many potential mechanisms have been proposed to perform the conversion between flow and thermal energy in a collisionless plasma, but the answer has been elusive. To solve the mystery, non-relativistic perpendicular and quasi-perpendicular shocks are studied via a coordinated approach employing cutting-edge numerical simulations with the continuum kinetic code Gkeyll and spacecraft observations supplied by NASA's Magnetospheric Multiscale (MMS) mission. In both sets of data, we employ novel techniques to identify how and where energy is exchanged between electromagnetic fields and plasma particles. By combining cutting-edge spacecraft data and numerical simulations with these diagnostics, we have been able to identify some of the primary mechanisms responsible electron heating and dissipation in heliospheric shocks. In addition to electron dynamics, we have complete access to proton simulation and in situ data, enabling us to characterize proton heating and acceleration in quasi-perpendicular shocks. Many astrophysical quasi-perpendicular shocks have similar parameters to those in this study, including supernova remnants and shocks in galaxy clusters. Additionally, the simulation code, Gkeyll, and associated analysis scripts are openly available for community use. This award has supported a collaborative effort between Princeton University, the University of Arizona - Tucson, and the University of Maryland - College Park. The award funded three early career scientists, including the funding and training of one female postdoctoral researcher, who was involved in all aspects of the MMS data analysis and some portions of the simulation analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Multi-sightline observation of narrow absorption lines in lensed quasar SDSS J1029+2623

We exploit the widely separated images of the lensed quasar SDSS J1029+2623 (z{sub em} = 2.197, θ = 22.″5) to observe its outflowing wind through two different sightlines. We present an analysis of three observations, including two with the Subaru telescope in 2010 February and 2014 April, separated by four years, and one with the Very Large Telescope, separated from the second Subaru observation by ∼2 months. We detect 66 narrow absorption lines (NALs), of which 24 are classified as intrinsic NALs that are physically associated with the quasar based on partial coverage analysis. The velocities of intrinsic NALs appear to cluster around values of v{sub ej} ∼ 59,000, 43,000, and 29,000 km s{sup −1}, which is reminiscent of filamentary structures obtained by numerical simulations. There are no common intrinsic NALs at the same redshift along the two sightlines, implying that the transverse size of the NAL absorbers should be smaller than the sightline distance between two lensed images. In addition to the NALs with large ejection velocities of v{sub ej} > 1000 km s{sup −1}, we also detect broader proximity absorption lines (PALs) at z{sub abs} ∼ z{sub em}. The PALs are likely to arise in outflowing gas at a distance of r ≤ 620 pc from the central black hole with an electron density of n {sub e} ≥8.7 × 10{sup 3} cm{sup −3}. These limits are based on the assumption that the variability of the lines is due to recombination. We discuss the implications of these results on the three-dimensional structure of the outflow.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Theoretical study of CO chemisorption on nickel and copper surfaces

A comparative study of the chemisorption of CO on Ni(001) and Cu(001) surfaces has been performed. The study is based on an analysis of the electronic structure of (M)5CO clusters obtained from self-consistent field X alpha scattered-wave calculations. The electron orbital formed principally by the 5 sigma orbital on CO was found to be mainly responsible for the bonding of the CO molecule to the metal surface for both the CO/Ni and CO/Cu systems. The different occupation of the antibonding 7 a sub 1 orbital in the two clusters is believed to be the major reason for the large difference observed in the measured heats of adsorption of CO on Ni and Cu surfaces. It was found also that metal atoms transfer electronic charge to the antibonding pi(asterisk)-like orbital of CO. A possible correlation between the amount of the charge transfered and the relative ease of dissociation of CO molecules on metal surfaces is discussed.

Yu, H. L.↗
NASA NTRS1980

X-ray astronomical spectroscopy

The current status of the X-ray spectroscopy of celestial X-ray sources, ranging from nearby stars to distant quasars, is reviewed. Particular emphasis is placed on the role of such spectroscopy as a useful and unique tool in the elucidation of the physical parameters of the sources. The spectroscopic analysis of degenerate and nondegenerate stellar systems, galactic clusters and active galactic nuclei, and supernova remnants is discussed.

Holt, S. S.↗
NASA NTRS1983

A new method for mapping multidimensional data to lower dimensions

A multispectral mapping method is proposed which is based on the new concept of BEND (Bidimensional Effective Normalised Difference). The method, which involves taking one sample point at a time and finding the interrelationships between its features, is found very economical from the point of view of storage and processing time. It has good dimensionality reduction and clustering properties, and is highly suitable for computer analysis of large amounts of data. The transformed values obtained by this procedure are suitable for either a planar 2-space mapping of geological sample points or for making grayscale and color images of geo-terrains. A few examples are given to justify the efficacy of the proposed procedure.

Gowda, K. C.↗