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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 OSTI2017

Pruning The ELM Survey: Characterizing Candidate Low-mass White Dwarfs through Photometric Variability

We assess the photometric variability of nine stars with spectroscopic T {sub eff} and log g values from the ELM Survey that locates them near the empirical extremely low-mass (ELM) white dwarf instability strip. We discover three new pulsating stars: SDSS J135512.34+195645.4, SDSS J173521.69+213440.6, and SDSS J213907.42+222708.9. However, these are among the few ELM Survey objects that do not show radial velocity (RV) variations that confirm the binary nature expected of helium-core white dwarfs. The dominant 4.31 hr pulsation in SDSS J135512.34+195645.4 far exceeds the theoretical cut-off for surface reflection in a white dwarf, and this target is likely a high-amplitude δ Scuti pulsator with an overestimated surface gravity. We estimate the probability to be less than 0.0008 that the lack of measured RV variations in four of eight other pulsating candidate ELM white dwarfs could be due to low orbital inclination. Two other targets exhibit variability as photometric binaries. Partial coverage of the 19.342 hr orbit of WD J030818.19+514011.5 reveals deep eclipses that imply a primary radius >0.4 R {sub ⊙}—too large to be consistent with an ELM white dwarf. The only object for which our time series photometry adds support to ELM white dwarf classification is SDSS J105435.78−212155.9, which has consistent signatures of Doppler beaming and ellipsoidal variations. We conclude that the ELM Survey contains multiple false positives from another stellar population at T {sub eff}≲9000 K, possibly related to the sdA stars recently reported from SDSS spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2023

The TRAPUM L -band survey for pulsars in Fermi -LAT gamma-ray sources

ABSTRACT More than 100 millisecond pulsars (MSPs) have been discovered in radio observations of gamma-ray sources detected by the Fermi Large Area Telescope (LAT), but hundreds of pulsar-like sources remain unidentified. Here, we present the first results from the targeted survey of Fermi-LAT sources being performed by the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. We observed 79 sources identified as possible gamma-ray pulsar candidates by a Random Forest classification of unassociated sources from the 4FGL catalogue. Each source was observed for 10 min on two separate epochs using MeerKAT’s L-band receiver (856–1712 MHz), with typical pulsed flux density sensitivities of $\sim 100\, \mu$Jy. Nine new MSPs were discovered, eight of which are in binary systems, including two eclipsing redbacks and one system, PSR J1526−2744, that appears to have a white dwarf companion in an unusually compact 5 h orbit. We obtained phase-connected timing solutions for two of these MSPs, enabling the detection of gamma-ray pulsations in the Fermi-LAT data. A follow-up search for continuous gravitational waves from PSR J1526−2744 in Advanced LIGO data using the resulting Fermi-LAT timing ephemeris yielded no detection, but sets an upper limit on the neutron star ellipticity of 2.45 × 10−8. We also detected X-ray emission from the redback PSR J1803−6707 in data from the first eROSITA all-sky survey, likely due to emission from an intrabinary shock.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

ML Classifier Fusion for Three Data Streams with Quality Inversely Proportional to Time Resolution

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Rao, Nageswara↗
DOE OSTI2018

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

VARIABLE STARS IN LARGE MAGELLANIC CLOUD GLOBULAR CLUSTERS. II. NGC 1786

This is the second in a series of papers studying the variable stars in Large Magellanic Cloud globular clusters. The primary goal of this series is to study how RR Lyrae stars in Oosterhoff-intermediate systems compare to their counterparts in Oosterhoff I/II systems. In this paper, we present the results of our new time-series B-V photometric study of the globular cluster NGC 1786. A total of 65 variable stars were identified in our field of view. These variables include 53 RR Lyraes (27 RRab, 18 RRc, and 8 RRd), 3 classical Cepheids, 1 Type II Cepheid, 1 Anomalous Cepheid, 2 eclipsing binaries, 3 Delta Scuti/SX Phoenicis variables, and 2 variables of undetermined type. Photometric parameters for these variables are presented. We present physical properties for some of the RR Lyrae stars, derived from Fourier analysis of their light curves. We discuss several different indicators of Oosterhoff type which indicate that the Oosterhoff classification of NGC 1786 is not as clear cut as what is seen in most globular clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Neural units with time-dependent functionality

We show that the time-resolved dynamics of an underdamped harmonic oscillator can be used to do multifunctional computation, performing distinct computations at distinct times within a single dynamical trajectory. We consider the amplitude of an oscillator whose inputs influence its frequency. The activity of the oscillator at fixed times is a nonmonotonic function of its inputs, so it can solve problems such as XOR that are not linearly separable. The activity of the oscillator at fixed input is a nonmonotonic function of time, so it is multifunctional in a temporal sense, and able to carry out distinct nonlinear computations at distinct times within the same dynamical trajectory. We show that a single oscillator, observed at different times, can act as all of the elementary logic gates and perform binary addition, the latter usually implemented in hardware using five logic gates. We show that a set of n oscillators, observed at different times, can perform an arbitrary number of analog-to-n-bit digital conversions. We also show that oscillators can be trained by gradient descent to perform distinct classification tasks at distinct times. Computing with time-dependent functionality can be done in or out of equilibrium, and suggests a way of reducing the number of parameters or devices required to do nonlinear computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

THE CURIOUS CASE OF GLASS I: HIGH IONIZATION AND VARIABILITY OF DIFFERENT TYPES

Our Spitzer IRS observation of the infrared companion Glass Ib revealed fine-structure emission with high ionization ([Ne III]/[Ne II] = 2.1 and [S IV]/[S III] = 0.6) that indicates that the gas is likely illuminated by hard radiation. While models suggest that extreme-ultraviolet radiation could be present in T Tauri stars, this is the first detection of [S IV] and such a high [Ne III]/[Ne II] ratio in a young star. We also find that Glass Ib displays the molecules HCN, CO{sub 2}, and H{sub 2}O in emission. Here we investigate the Glass I binary system and consider possible mechanisms that may have caused the high ionization, whether from an outflow or disk irradiation. We also model the spectral energy distributions of Glass Ia and Ib to test if the system is a young member of the Chameleon I star-forming region, and we consider other possible classifications for the system. We find that Glass Ib is highly variable, showing changes in continuum strength and emission features at optical, near-infrared, and mid-infrared wavelengths. The optical light curve indicates that a central stellar component in Glass Ib became entirely visible for 2.5 years beginning in mid-2002 and possibly displayed periodic variability with repeated, short-period dimming during that time. As the fine-structure emission was not detected in observations before or after our Spitzer IRS observation, we explore whether the variable nature of Glass Ib is related to the gas being highly ionized, possibly due to variable accretion or an X-ray flare.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

SPECTRAL CLASSIFICATION OF THE BRIGHTEST OBJECTS IN THE GALACTIC STAR-FORMING REGION W40

We present high signal-to-noise, moderate resolution (R Almost-Equal-To 2000) near-infrared spectra, as well as 10 {mu}m imaging, for the brightest members of the central stellar cluster in the W40 H II region, obtained using the SpeX and MIRSI instruments at NASA's Infrared Telescope Facility. Using these observations combined with archival Spitzer Space Telescope data, we have determined the spectral classifications, extinction, distances, and spectral energy distributions (SEDs) for the brightest members of the cluster. Of the eight objects observed, we identify four main-sequence (MS) OB stars (one late-O, three early-B), two Herbig Ae/Be stars, and two low-mass young stellar objects (Class II). Strong He I absorption at 1.083 {mu}m in the MS star spectra strongly suggests that at least some of these sources are in fact close binaries. Two out of the four MS stars also show significant infrared excesses typical of circumstellar disks. Extinctions and distances were determined for each MS star by fitting model stellar atmospheres to the SEDs. We estimate a distance to the cluster of between 455 and 535 pc, which agrees well with earlier (but far less precise) distance estimates. We conclude that the late-O star we identify is the dominant source of Lyman continuum luminosity needed to power the W40 H II region and is the likely source of the stellar wind that has blown a large ( Almost-Equal-To 4 pc) pinched-waist bubble observed in wide-field mid-IR images. We also suggest that 3.6 cm radio emission observed from some of the sources in the cluster is likely not due to emission from ultracompact H II regions, as suggested in other work, due to size constraints based on our derived distance to the cluster. Finally, we also present a discussion of the curious source IRS 3A, which has a very strong mid-IR excess (despite its B3 MS classification) and appears to be embedded in a dusty envelope roughly 2700 AU in size.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SEARCHING FOR NEW YELLOW SYMBIOTIC STARS: POSITIVE IDENTIFICATION OF StHα63

Yellow symbiotic stars are useful targets for probing whether mass transfer has happened in their binary systems. However, the number of known yellow symbiotic stars is very scarce. We report spectroscopic observations of five candidate yellow symbiotic stars that were selected by their positions in the 2MASS (J − H) versus (H − K{sub s}) diagram and which were included in some emission-line catalogs. Among the five candidates, only StHα63 is identified as a new yellow symbiotic star because of its spectrum and its position in the [TiO]{sub 1}–[TiO]{sub 2} diagram, which indicates a K4–K6 spectral type. In addition, the derived electron density (∼10{sup 8.4} cm{sup −3}) and several emission-line intensity ratios provide further support for that classification. The other four candidates are rejected as symbiotic stars because three of them actually do not show emission lines and the fourth one only Balmer emission lines. We also found that the WISE W3–W4 index clearly separates normal K-giants from yellow symbiotic stars and therefore can be used as an additional tool for selecting candidate yellow symbiotic stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

MULTI-WAVELENGTH STUDY OF HESS J1741–302

We present the results of two Chandra X-ray Observatory (CXO) observations of TeV γ-ray source HESS J1741–302. We investigate whether there is any connection between HESS J1741−302 and the sources seen at lower energies. One of the brightest X-ray sources in the HESS J1741–302 field, CXOU J174112.1−302908, appears to be associated with a low-mass star (possibly representing a quiescent low-mass X-ray binary or cataclysmic variable (CV)), hence, it is unlikely to be a source of TeV γ-rays. In the same field we have potentially detected X-rays from WR 98a, which is likely to be a colliding wind binary with massive stars. No TeV emission has been reported so far from such systems although predictions have been made. Finally, we found that the previously reported Suzaku source, Suzaku J1740.5–3014 (which is not covered by the CXO observations), appears to be a hard X-ray source detected by INTERGAL ISGRI, which supports the magnetized CV classification but makes its association with the TeV emission unlikely. The young pulsar PSR B1737–30, so far undetected in X-rays and projected on the sky near the CV, may be the contributor of relativistic particles responsible for the TeV emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗
DOE OSTI2017

Ejection of the Massive Hydrogen-rich Envelope Timed with the Collapse of the Stripped SN 2014C

We present multi-wavelength observations of SN 2014C during the first 500 days. These observations represent the first solid detection of a young extragalactic stripped-envelope SN out to high-energy X-rays ∼40 keV. SN 2014C shows ordinary explosion parameters ( E {sub k} ∼ 1.8 × 10{sup 51} erg and M {sub ej} ∼ 1.7 M{sub ⊙}). However, over an ∼1 year timescale, SN 2014C evolved from an ordinary hydrogen-poor supernova into a strongly interacting, hydrogen-rich supernova, violating the traditional classification scheme of type-I versus type-II SNe. Signatures of the SN shock interaction with a dense medium are observed across the spectrum, from radio to hard X-rays, and revealed the presence of a massive shell of ∼1 M {sub ⊙} of hydrogen-rich material at ∼6 × 10{sup 16} cm. The shell was ejected by the progenitor star in the decades to centuries before collapse. This result challenges current theories of massive star evolution, as it requires a physical mechanism responsible for the ejection of the deepest hydrogen layer of H-poor SN progenitors synchronized with the onset of stellar collapse. Theoretical investigations point at binary interactions and/or instabilities during the last nuclear burning stages as potential triggers of the highly time-dependent mass loss. We constrain these scenarios utilizing the sample of 183 SNe Ib/c with public radio observations. Our analysis identifies SN 2014C-like signatures in ∼10% of SNe. This fraction is reasonably consistent with the expectation from the theory of recent envelope ejection due to binary evolution if the ejected material can survive in the close environment for 10{sup 3}–10{sup 4} years. Alternatively, nuclear burning instabilities extending to core C-burning might play a critical role.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

D-Wave for High Energy Physics

aWe analyze different problems in high-energy physics where quantum computing, and namely quantum annealers, may be able to provide an edge in coming years: 1) The training of Restricted Boltzmann Machines (RBM) is a natural problem for quantum annealers. We explore RBM trained on a D-Wave machine for classification of galaxy morphologies, including a discussion of how to obtain a Boltzmann distribution from the annealer. 2) For this study, we cast the problem of explaining and predicting the production of particles in high energy collisions (color recombination) as an optimization problem and employ classical and quantum computing techniques to find solutions. In particular, we present a quadratic unconstrained binary optimization (QUBO) formulation for quantum annealers. In parallel to these efforts, we also present the cross-platform XACC package for quantum compilation and execution.

Caldeira, J.↗
DOE OSTI2023

Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Using Long-term Millisecond Pulsar Timing to Obtain Physical Characteristics of the Bulge Globular Cluster Terzan 5

Over the past decade, the discovery of three unique stellar populations and a large number of confirmed pulsars within the globular cluster Terzan 5 has raised questions over its classification. Using the long-term radio pulsar timing of 36 ms pulsars in the cluster core, we provide new measurements of key physical properties of the system. As Terzan 5 is located within the galactic bulge, stellar crowding and reddening make optical and near-infrared observations difficult. Pulsar accelerations, however, allow us to study the intrinsic characteristics of the cluster independent of reddening and stellar crowding and probe the mass density profile without needing to quantify the mass-to-light ratio. Relating the spin and orbital periods of each pulsar to the acceleration predicted by a King model, we find a core density of 1.58{sub −0.13}{sup +0.13} × 10{sup 6} M{sub ⊙} pc{sup −3}, a core radius of 0.16{sub −0.01}{sup +0.01} pc, a pulsar density profile of n∝r{sup −3.14{sub −}{sub 0}{sub .}{sub 5}{sub 3}{sup +{sup 0{sup .{sup 5{sup 2}}}}}}, and a total mass of M{sub T}(R{sub ⊥}<1.0 pc) ≃ 3.0 × 10{sup 5} M{sub ⊙}, assuming a cluster distance of 5.9 kpc. Using this information, we argue against Terzan 5 being a disrupted dwarf galaxy and discuss the possibility of it being a fragment of the Milky Way’s proto-bulge. We also discuss whether low-mass pulsars were formed via electron-capture supernovae or exist in a core full of heavy white dwarfs and hard binaries. Finally, we provide an upper limit for the mass of a possible black hole at the core of the cluster of M{sub BH}≃3×10{sup 4} M{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Empirical databases of crystal structures and thermodynamic properties are fundamental tools for materials research. Recent rapid proliferation of computational data on materials properties presents the possibility to complement and extend the databases where the experimental data is lacking or difficult to obtain. Enhanced repositories that integrate both computational and empirical approaches open novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds, metastable structures and correlations between various characteristics. The practical realization of these opportunities depends on a systematic compilation and classification of the generated data in addition to an accessible interface for the materials science community. In this paper we present an extensive repository, aflowlib.org, comprising phase-diagrams, electronic structure and magnetic properties, generated by the high-throughput framework AFLOW. This continuously updated compilation currently contains over 150,000 thermodynamic entries for alloys, covering the entire composition range of more than 650 binary systems, 13,000 electronic structure analyses of inorganic compounds, and 50,000 entries for novel potential magnetic and spintronics systems. The repository is available for the scientific community on the website of the materials research consortium, aflowlib.org.

Materials Science↗