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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

SU-E-T-50: Automatic Validation of Megavoltage Beams Modeled for Clinical Use in Radiation Therapy

Purpose: To automatically validate megavoltage beams modeled in XiO™ 4.50 (Elekta, Stockholm, Sweden) and Varian Eclipse™ Treatment Planning Systems (TPS) (Varian Associates, Palo Alto, CA, USA), reducing validation time before beam-on for clinical use. Methods: A software application that can automatically read and analyze DICOM RT Dose and W2CAD files was developed using MatLab integrated development environment.TPS calculated dose distributions, in DICOM RT Dose format, and dose values measured in different Varian Clinac beams, in W2CAD format, were compared. Experimental beam data used were those acquired for beam commissioning, collected on a water phantom with a 2D automatic beam scanning system.Two methods were chosen to evaluate dose distributions fitting: gamma analysis and point tests described in Appendix E of IAEA TECDOC-1583. Depth dose curves and beam profiles were evaluated for both open and wedged beams. Tolerance parameters chosen for gamma analysis are 3% and 3 mm dose and distance, respectively.Absolute dose was measured independently at points proposed in Appendix E of TECDOC-1583 to validate software results. Results: TPS calculated depth dose distributions agree with measured beam data under fixed precision values at all depths analyzed. Measured beam dose profiles match TPS calculated doses with high accuracy in both open and wedged beams. Depth and profile dose distributions fitting analysis show gamma values < 1. Relative errors at points proposed in Appendix E of TECDOC-1583 meet therein recommended tolerances.Independent absolute dose measurements at points proposed in Appendix E of TECDOC-1583 confirm software results. Conclusion: Automatic validation of megavoltage beams modeled for their use in the clinic was accomplished. The software tool developed proved efficient, giving users a convenient and reliable environment to decide whether to accept or not a beam model for clinical use. Validation time before beam-on for clinical use was reduced to a few hours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Three-dimensional potential energy surfaces of ArNO ($\tilde{X}$ 2 Π)

Until now, the potential energy surfaces (PESs) of the ArNO complex found in the literature were two-dimensional, with the NO interatomic distance being fixed. In this work, we present the first accurate three-dimensional ground state X 2 Π PESs (both A ' and A ") of ArNO computed at the CCSD(T)/CBS level of theory. The equilibrium geometries and the well depths ( D e ) are compared to several other electronic structure methods. We found that using the multireference method, MRCI-F12 makes the surfaces much shallower (by 25%) and the depth of the surfaces does not agree with experimental data. The explicitly correlated coupled-cluster method underestimates the well depth as well. Analytic representations for both A ' and A " surfaces were fit to 4380 ab initio points to within 2.71 cm -1 .A three-dimensional Numerov propagator method in Delves coordinates is used to compute the bound state spectrum up to J tot = 6.5. The recommended dissociation energies are D 0 = 97.2 cm -1 for the adiabatic ground state and D e = 133.7 (128.1) cm -1 for A ' ( A ")

36 MATERIALS SCIENCE↗
DOE OSTI2012

Mass composition of 10{sup 17}- to 10{sup 18}-eV primary cosmic rays according to data on the lateral distribution of radio emission from extensive air showers

Experimental data obtained for the lateral distribution of radio emission from extensive air showers (EAS) at the array of Moscow State University (30-34 MHz) and the LOPES array (40-80 MHz) were comparedwith the results of calculations performed within amicroscopic approach based on aMonte Carlo simulation of EAS (CORSIKA code). The same experimental data were used to reconstruct the distribution of the depth of the EAS maximum at cosmic-ray energies in the range of 1017-1018 eV. The energy dependence of the depth of the EAS maximum was constructed for the case of data from the LOPES array, and the mass composition of cosmic rays was estimated for this case. From the resulting dependences, it follows that the mass composition shows a trend toward becoming lighter in the energy range being considered.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Hawthorne Nevada Deep Direct-Use Feasibility Study - Navy Geothermal Program Office General Data

The objective of this project is to use a multi-disciplinary, three-tiered approach to assess the geothermal resource and determine the feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the various town and county facilities in Hawthorne, Nevada. This assessment directly targets a geothermal resource recently characterized by the Navy Geothermal Program Office (GPO) as part of a focused exploration and development campaign. This data set contains: Excel file of the temperature at depth logs; Zip file containing shapefiles of gravity magnitude and base data for the Hawthorne Geothermal area; Word doc files containing HWAD geothermal report, figures and local stratigraphy; Zip folder containing PDF files of relevant HWAD document; Zip folder containing JPEGs of the John Oldow maps; Zip folder containing MXDs, xls, and dbf files of the Hawthorne Geothermal area and figures; Zip folder containing Hawthorne Geothermal data from Epsilon

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Precipitation Depth-Duration-Frequency Analysis for the Nevada National Security Site and Surrounding Areas

Accurate precipitation frequency data are important for Environmental Management Soils Activities on the Nevada National Security Site (NNSS). These data are important for environmental assessments performed for regulatory closure of Soils Corrective Action Unit (CAU) Sites, as well as engineering mitigation designs and post-closure monitoring strategies to assess and minimize potential contaminant migration from Soils CAU Sites. Although the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 (Bonnin et al., 2011) provides precipitation frequency data for the NNSS area, the NNSS-specific observed precipitation data were not consistent with the NOAA Atlas 14 predicted data. This is primarily due to the NOAA Atlas 14 products being produced from analyses without including the approximately 30 NNSS precipitation gage records, several of which approach or exceed 50 year of record. Therefore, a study of precipitation frequency that incorporated the NNSS precipitation gage records into the NOAA Atlas 14 dataset, was performed specifically for the NNSS to derive more accurate site-specific precipitation data products. Precipitation frequency information, such as the depth-duration-frequency (DDF) relationships, are required to generate synthetic standard design storm hydrographs and assess actual precipitation events. In this study, the actual long-term NNSS precipitation gage records, some of which are the longest gage records in southern and central Nevada, were analyzed to allow for more accurate precipitation DDF estimates to be developed for the NNSS. Gridded maps of precipitation frequency for the NNSS and surrounding areas were then produced.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Review of the Influence of Processing Parameters on ODS Steels Produced via Additive Manufacturing Techniques

Abstract This paper reviews current observations regarding processing conditions for oxide dispersion-strengthened steels consolidated through additive manufacturing techniques. Variations in ODS steels observed across process parameters include changes in grain size, grain texture, oxide size, density of oxides, porosity, melt pool characteristics, and mechanical properties. These properties were then compared across techniques to understand which techniques and processing conditions lead to the highest strength, ductility, and oxide density. Current literature suggests that a mix of grain types, in the form of either morphology or phase, can significantly increase the strength of printed ODS steels. Meanwhile, the most ductile samples, regardless of consolidation technique or matrix material, were made from feedstock with oxide additions located on the powder surface. Reported grain and oxide sizes were plotted against the ratio of laser power to scan speed, volumetric energy density, and normalized enthalpy. No strong correlation between these values and microstructural features was observed. The plots that were made suggest that a larger data set, more in-depth representative equations, and more defined material properties as a function of specific feedstock used are necessary to determine a value that can be correlated to the printed ODS steel microstructure.

deJong, Matthew↗
DOE OSTI2022

On the thermal oxidation of nuclear graphite relevant to high-temperature gas cooled reactors

Thermal oxidation of nuclear graphite components is highly undesirable because it can cause structural and property degradation that negatively affect a reactor's intended operation. In this work, the body of knowledge of nuclear graphite oxidation is highlighted, including when O 2 , H 2 O, and/or CO 2 are the oxidant. Oxidation conditions relevant to high-temperature gas-cooled reactors (i.e., when oxidation could occur either as an acute or chronic phenomenon) are emphasized. Here, the objective is to summarize graphite oxidation data in a practical and accessible way to inform future research and regulatory requirements. Although each grade of nuclear graphite is different, the oxidation mechanism has underlying commonalities. Although oxidation behavior is grade dependent, the general temperature dependence is well described by a sequence of elementary steps which become rate limiting. Because the regime transition temperature depends on sample microstructure, size, and oxidant supply rate, extrapolating results beyond the experimental range should be done cautiously. Gravimetric oxidation rate measurements generally replicate well. However, caution must be exercised when rates are estimated by other methods or for samples that deviate significantly in size. Air oxidation data for IG-110, NBG-18, and PCEA graphite is critically reviewed to emphasize this point. Despite the amount of experimental data, gaps remain. Sample size and shape effects are not fully explained. Data on the oxidant penetration depth are insufficient. Analytical assessments demonstrate that lower temperature oxidation does not necessarily imply that oxidation is uniform throughout the bulk. Oxidation occurs faster at higher temperature but is more localized to the exposed surface. Paradoxically, at equal mass loss percentage, low temperature oxidation leads to greater property degradation than at high temperature. The isolated effect of oxidation is important; however, a gap remains in the systematic understanding of any potential effect of neutron irradiation on graphite structure and reactivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

To grind or not to grind? The influence of mechanical and thermal treatments on the $\mathrm{Cu/Zn}$ disorder in $\mathrm{Cu}$ 2 $\mathrm{ZnSn}$($\mathrm{S}$ x $\mathrm{Se}$ 1-x ) 4 monograins

We report that Kesterite-type based thin film solar cell technologies are mainly based on polycrystalline absorber layers. A promising low cost alternative technology uses Cu 2 ZnSn(S x Se 1-x ) 4 (CZTSSe) monograins (single crystals of 20–100 μm size) which are fixed in a polymer matrix to form a flexible solar cell. The Cu/Zn disorder is discussed as a possible reason for band tailing causing voltage losses limiting the efficiency of CZTSSe-based devices. The experimental determination of the order parameter Q which is a quantitative measure of Cu/Zn disorder, requires a differentiation between the isoelectronic cations Cu + and Zn 2+ . An in-depth analysis of neutron diffraction data allows the determination of type and concentration of intrinsic point defects including a distinction between Cu and Zn. Neutron diffraction requires large sample volumes, thus monograins offer the unique possibility to correlate structural disorder in CZTSSe with device performance parameters. In this study we tackle the influence of grinding the monograins on stoichiometry deviations, the Cu/Zn disorder as well as intrinsic point defects and optoelectronic properties of CZTSSe monograins. Moreover, an easy methodology based on Raman scattering spectroscopy is proposed for the assessment of Cu/Zn disorder in the CZTSSe compounds.

14 SOLAR ENERGY↗
DOE OSTI2023

Catalytic reforming of methane with H 2 S via dynamically stabilized sulfur on transition metal oxides and sulfides

Reforming of methane with H 2 S is a promising path to directly utilize sour natural gas reserves, although some aspects of the mechanism and structure–function relations remain elusive. In this report we show that metal oxides of group 4–6 elements, which are inert for steam and dry methane reforming reactions, are active and stable (pre)catalysts for the H 2 S reforming of methane. The key active sites are sulfur species (S*) that are dynamically bound to metal cations during catalysis. Similar H/D isotope exchange patterns and universal rate inhibition by H 2 on representative catalysts indicate that H 2 S decomposition and recombination of surface hydrogen atoms are quasi-equilibrated, whereas CH 4 dissociation steps are reversible, yet not quasi-equilibrated. An in-depth analysis of the kinetic data and isotopic substitution effects identifies S*-mediated C–H bond cleavage as the most plausible rate-limiting step common for all catalysts, with subtle yet essential differences between 3d and 4d/5d catalysts in the thermodynamic stability of S*.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Development of a Fast-Spectrum Self-Powered Neutron Detector for Molten Salt Experiments in the Versatile Test Reactor

The self-powered neutron detector (SPND) is a widely used flux monitor in thermal nuclear reactors. Although this is a mature technology, the current state of the art is tuned for a thermal neutron spectrum, so many of the devices currently in use lack sensitivity to fast neutrons. Because current in SPNDs is produced through nuclear reactions with the neutron flux inside a reactor, sensitivity in SPNDs is determined by the neutron cross section of the neutron-sensitive portion of the detector, termed the emitter. This neutron cross section drops by orders of magnitude between thermal and fast neutron energies for many emitters in currently used SPNDs, with a corresponding drop in current from the detector. This paper discusses efforts to develop a fast-spectrum self-powered neutron detector (FS-SPND) that is sensitive to neutrons with energies ranging from 0.025 eV up to 1 MeV. An in-depth analysis of Evaluated Nuclear Data File (ENDF)/B-VII.1 neutron-capture cross sections was performed, and four new materials were identified that are suitable emitter candidates for use in measuring fast neutrons. All four materials are stable mid-shell nuclei in the region between doubly magic 132Sn and 208Pb. Each candidate was simulated with the Geant4 Monte Carlo simulation toolkit to optimize overall detector efficiency.

Goetz, Callie↗
DOE OSTI2011

SPECTROSCOPY OF LUMINOUS z > 7 GALAXY CANDIDATES AND SOURCES OF CONTAMINATION IN z > 7 GALAXY SEARCHES

We present three bright z{sup +}-dropout candidates selected from deep near-infrared (NIR) imaging of the COSMOS 2 deg{sup 2} field. All three objects match the 0.8-8 {mu}m colors of other published z > 7 candidates but are 3 mag brighter, facilitating further study. Deep spectroscopy of two of the candidates covering 0.64-1.02 {mu}m with Keck-DEIMOS and all three covering 0.94-1.10 {mu}m and 1.52-1.80 {mu}m with Keck-NIRSPEC detects weak spectral features tentatively identified as Ly{alpha} at z = 6.95 and z = 7.69 in two of the objects. The third object is placed at z {approx} 1.6 based on a 24 {mu}m and weak optical detection. A comparison with the spectral energy distributions of known z < 7 galaxies, including objects with strong spectral lines, large extinction, and large systematic uncertainties in the photometry, yields no objects with similar colors. However, the {lambda} > 1 {mu}m properties of all three objects can be matched to optically detected sources with photometric redshifts at z {approx} 1.8, so the non-detection in the i {sup +} and z {sup +} bands is the primary factor which favors a z > 7 solution. If any of these objects are at z {approx} 7, the bright end of the luminosity function is significantly higher at z > 7 than suggested by previous studies, but consistent within the statistical uncertainty and the dark matter halo distribution. If these objects are at low redshift, the Lyman break selection must be contaminated by a previously unknown population of low-redshift objects with very strong breaks in their broadband spectral energy distributions and blue NIR colors. The implications of this result on luminosity function evolution at high redshift are discussed. We show that the primary limitation of z > 7 galaxy searches with broad filters is the depth of the available optical data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

NEW CONSTRAINTS ON COMPANIONS AND DUST WITHIN A FEW AU OF VEGA

We report on high contrast near-infrared ({approx}2.2 {mu}m) observations of Vega obtained with the Palomar Fiber Nuller, a dual sub-aperture rotating coronagraph installed at the Palomar Hale telescope. The data show consistent astrophysical null depth measurements at the {approx_equal} 10{sup -3} level or below for three different baseline orientations spanning 60 deg in azimuth, with individual 1{sigma} uncertainties {<=}7 x 10{sup -4}. These high cancellation and accuracy levels translate into a dynamic range greater than 1000:1 inside the diffraction limit of the 5 m telescope beam. Such high contrast performance is unprecedented in the near-infrared and provides improved constraints on Vega's immediate ({approx_equal}20 to 250 mas, or {approx_equal}0.15 to 2 AU) environment. In particular, our measurements rule out any potential companion in the [0.25-1 AU] region contributing more than 1% of the overall near-infrared stellar flux, with limits as low as 0.2% near 0.6 AU. These are the best upper limits established so far by direct detection for a companion to Vega in this inner region. We also conclude that any dust population contributing a significant ({>=}1%) near-infrared thermal excess can arise only within 0.2 AU of the star, and that it must consist of much smaller grains than in the solar zodiacal cloud. Dust emission from farther than {approx_equal}2 AU is also not ruled out by our observations, but would have to originate in strong scattering, pointing again to very small grains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The radio relics and halo of El Gordo, a massive z = 0.870 cluster merger

We present 610 MHz and 2.1 GHz imaging of the massive Sunyaev-Zel'dovich Effect selected z = 0.870 cluster merger ACT-CL J0102–4915 ({sup E}l Gordo{sup )}, obtained with the Giant Metrewave Radio Telescope and the Australia Telescope Compact Array (ATCA), respectively. We detect two complexes of radio relics separated by 3.'4 (1.6 Mpc) along the system's northwest-to-southeast collision axis that have high integrated polarization fractions (33%) and steep spectral indices (α between 1 and 2; S {sub ν}∝ν{sup –α}), consistent with creation via Fermi acceleration by shocks in the intracluster medium triggered by the cluster collision. From the spectral index of the relics, we compute a Mach number M=2.5{sub −0.3}{sup +0.7} and shock speed of 2500{sub −300}{sup +400} km s{sup −1}. With our wide-bandwidth, full-polarization ATCA data, we compute the Faraday depth φ across the northwest relic and find a range of values spanning Δφ = 30 rad m{sup –2}, with a mean value of (φ) = 11 rad m{sup –2} and standard deviation σ{sub φ} = 6 rad m{sup –2}. With the integrated line-of-sight gas density derived from new Chandra X-ray observations, our Faraday depth measurement implies B {sub ∥} ∼ 0.01 μG in the cluster outskirts. The extremely narrow shock widths in the relics (d {sub shock} ≤ 23 kpc), caused by the short synchrotron cooling timescale of relativistic electrons at z = 0.870, prevent us from placing a meaningful constraint on the magnetic field strength B using cooling time arguments. In addition to the relics, we detect a large (r {sub H} ≅ 1.1 Mpc radius), powerful (log (L {sub 1.4}/W Hz{sup –1}) = 25.66 ± 0.12) radio halo with a shape similar to El Gordo's 'bullet'-like X-ray morphology. The spatially resolved spectral-index map of the halo shows the synchrotron spectrum is flattest near the relics, along the system's collision axis, and in regions of high T {sub gas}, all locations associated with recent energy injection. The spatial and spectral correlation between the halo emission and cluster X-ray properties supports primary-electron processes like turbulent reacceleration as the halo production mechanism. The halo's integrated 610 MHz to 2.1 GHz spectral index is a relatively flat α = 1.2 ± 0.1, consistent with the cluster's high T {sub gas} in view of previously established global scaling relations. El Gordo is the highest-redshift cluster known to host a radio halo and/or radio relics, and provides new constraints on the non-thermal physics in clusters at z > 0.6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

AEGIS-X: Deep Chandra imaging of the central Groth strip

We present the results of deep Chandra imaging of the central region of the Extended Groth Strip, the AEGIS-X Deep (AEGIS-XD) survey. When combined with previous Chandra observations of a wider area of the strip, AEGIS-X Wide (AEGIS-XW), these provide data to a nominal exposure depth of 800 ks in the three central ACIS-I fields, a region of approximately 0.29 deg{sup 2}. This is currently the third deepest X-ray survey in existence; a factor ∼2−3 shallower than the Chandra Deep Fields (CDFs), but over an area ∼3 times greater than each CDF. We present a catalog of 937 point sources detected in the deep Chandra observations, along with identifications of our X-ray sources from deep ground-based, Spitzer, GALEX, and Hubble Space Telescope imaging. Using a likelihood ratio analysis, we associate multiband counterparts for 929/937 of our X-ray sources, with an estimated 95% reliability, making the identification completeness approximately 94% in a statistical sense. Reliable spectroscopic redshifts for 353 of our X-ray sources are available predominantly from Keck (DEEP2/3) and MMT Hectospec, so the current spectroscopic completeness is ∼38%. For the remainder of the X-ray sources, we compute photometric redshifts based on multiband photometry in up to 35 bands from the UV to mid-IR. Particular attention is given to the fact that the vast majority the X-ray sources are active galactic nuclei and require hybrid templates. Our photometric redshifts have mean accuracy of σ=0.04 and an outlier fraction of approximately 5%, reaching σ=0.03 with less than 4% outliers in the area covered by CANDELS . The X-ray, multiwavelength photometry, and redshift catalogs are made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Challenging Geographical Disadvantages and Social Exclusion: A Case Study of Gendered Rural Transformation in Mountain Villages in the Western Black Sea Region of Turkey

Abstract This article discusses the gender‐specific effect of the changing economic structure of mountain villages in Turkey. Rural women, who are conventionally unpaid family workers, have begun entering into off‐farm work due to declining household incomes in recent years. The implications of this for gender relations are found to be contradictory in much recent research. In this study, we employed the concepts of the hybridity of rural communities and the multiplicity of social exclusion in a globalising countryside. We used a qualitative methodology employing multiple sources of data. In addition to in‐depth interviews with 27 village women, we conducted semi‐structured interviews with village headmen and structured interviews with 218 village women to understand the structural context behind the women’s narratives. We found that the dimension of social exclusion varies with gender and age within the community. While middle‐aged men are increasingly unemployed and have withdrawn into uncompetitive rural life, young women engage in wage work and challenge geographical disadvantages by spending their meagre earnings on transportation and communication technologies. The reconfiguration of marginality within the community, rather than the total marginalisation of villagers, is an ongoing process.

Suzuki Him, Miki↗