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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 OSTI2019

GROWTH on S190510g: DECam Observation Planning and Follow-up of a Distant Binary Neutron Star Merger Candidate

The first two months of the third Advanced LIGO and Virgo observing run (2019 April-May) showed that distant gravitational-wave (GW) events can now be readily detected. Three candidate mergers containing neutron stars (NS) were reported in a span of 15 days, all likely located more than 100 Mpc away. However, distant events such as the three new NS mergers are likely to be coarsely localized, which highlights the importance of facilities and scheduling systems that enable deep observations over hundreds to thousands of square degrees to detect the electromagnetic counterparts. On 2019 May 10 02:59:39.292 UT the GW candidate S190510g was discovered and initially classified as a binary neutron star (BNS) merger with 98% probability. The GW event was localized within an area of 3462 deg 2 , later refined to 1166 deg 2 (90%) at a distance of 227 ± 92 Mpc. We triggered Target-of-Opportunity observations with the Dark Energy Camera (DECam), a wide-field optical imager mounted at the prime focus of the 4 m Blanco Telescope at Cerro Tololo Inter-American Observatory in Chile. This Letter describes our DECam observations and our real-time analysis results, focusing in particular on the design and implementation of the observing strategy. Within 24 hr of the merger time, we observed 65% of the total enclosed probability of the final skymap with an observing efficiency of 94%. Here, we identified and publicly announced 13 candidate counterparts. S190510g was reclassified 1.7 days after the merger, after our observations were completed, with a "BNS merger" probability reduced from 98% to 42% in favor of a "terrestrial classification.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Barium stars as tracers of s -process nucleosynthesis in AGB stars: II. Using machine learning techniques on 169 stars

Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process (s-process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the FRUITY and Monash stellar models. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s-process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 M ⊙ and 2.34 M ⊙ and an average [Fe/H] = –0.21 and –0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2023

High-velocity CP2 stars in the Galactic halo

The second subclass of chemically peculiar stars, the CP2 stars, are early-type stars exhibiting anomalous abundances with variable line strengths possibly also accompanied by photometric variability that typically belong to the Galactic disk. However, a small fraction of these objects were recently found to be located far from the Galactic plane and are thought to belong to the Galactic halo, which is unexpected for this class of objects. Our study investigates the halo membership of the proposed halo CP2 star candidates based on their velocities and Galactic orbits, to determine their points of origin. In addition, we searched for further halo CP2 star candidates using an as-yet-unpublished sample of CP stars. Our analysis relied on the astrometry from the early third Gaia data release and radial velocities based on the spectroscopy from LAMOST and SDSS, which was also employed in spectral classification. The light variability of the confirmed CP2 stars in our sample was analyzed using data from the ZTF and ATLAS surveys. After filtering our initial sample using kinematic and spectroscopic criteria, we identified six CP2 stars with kinematical properties consistent with a halo membership. The orbits of these stars are in agreement with an origin in the Galactic disk where they were probably ejected through dynamical interactions or in the binary supernova scenario, making them the first runaway CP2 stars known.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Carbon Storage and Enhanced Oil Recovery in Pennsylvanian Morrow Formation Clastic Reservoirs: Controls on Oil–Brine and Oil–CO2 Relative Permeability from Diagenetic Heterogeneity and Evolving Wettability

The efficiency of carbon utilization and storage within the Pennsylvanian Morrow B sandstone, Farnsworth Unit, Texas, is dependent on three-phase oil, brine, and CO2 flow behavior, as well as spatial distributions of reservoir properties and wettability. We show that end member two-phase flow properties, with binary pairs of oil–brine and oil–CO2, are directly dependent on heterogeneity derived from diagenetic processes, and evolve progressively with exposure to CO2 and changing wettability. Morrow B sandstone lithofacies exhibit a range of diagenetic processes, which produce variations in pore types and structures, quantified at the core plug scale using X-ray micro computed tomography imaging and optical petrography. Permeability and porosity relationships in the reservoir permit the classification of sedimentologic and diagenetic heterogeneity into five distinct hydraulic flow units, with characteristic pore types including: macroporosity with little to no clay filling intergranular pores; microporous authigenic clay-dominated regions in which intergranular porosity is filled with clay; and carbonate–cement dominated regions with little intergranular porosity. Steady-state oil–brine and oil–CO2 co-injection experiments using reservoir-extracted oil and brine show that differences in relative permeability persist between flow unit core plugs with near-constant porosity, attributable to contrasts in and the spatial arrangement of diagenetic pore types. Core plugs “aged” by exposure to reservoir oil over time exhibit wettability closer to suspected in situ reservoir conditions, compared to “cleaned” core plugs. Together with contact angle measurements, these results suggest that reservoir wettability is transient and modified quickly by oil recovery and carbon storage operations. Reservoir simulation results for enhanced oil recovery, using a five-spot pattern and water-alternating-with-gas injection history at Farnsworth, compare models for cumulative oil and water production using both a single relative permeability determined from history matching, and flow unit-dependent relative permeability determined from experiments herein. Both match cumulative oil production of the field to a satisfactory degree but underestimate historical cumulative water production. Differences in modeled versus observed water production are interpreted in terms of evolving wettability, which we argue is due to the increasing presence of fast paths (flow pathways with connected higher permeability) as the reservoir becomes increasingly water-wet. The control of such fast-paths is thus critical for efficient carbon storage and sweep efficiency for CO2-enhanced oil recovery in heterogeneous reservoirs.

Rasmussen, Lindsey↗
DOE OSTI2023

Simulations of Precessing Jets and the Formation of X-shaped Radio Galaxies

Abstract Jet precession is sometimes invoked to explain asymmetries in radio galaxy (RG) jets and “X/S/Z-shaped” RGs, caused by the presence of a binary black hole companion to the source active galactic nucleus or by accretion instabilities. We present a series of simulations of RG jet precession to examine how these sources would evolve over time, including a passive distribution of cosmic-ray electrons so we can model radio synchrotron emissions and create synthetic radio maps of the sources. We find that a single source viewed from different angles can result in differing RG morphological classifications, confusing physical implications of these classifications. Additionally, the jet trajectories can become unstable due to their own self-interactions and lead to “reorientation events” that may look like the effects of external dynamics such as shocks, winds, or cold fronts in the medium. Finally, something akin to an “Odd Radio Circle” may be observed in the case of viewing the radio remnant of such a precessing source from a line of sight near the precession axis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The Norma arm region Chandra survey catalog: X-ray populations in the spiral arms

We present a catalog of 1415 X-ray sources identified in the Norma Arm Region Chandra Survey (NARCS), which covers a 2° × 0.°8 region in the direction of the Norma spiral arm to a depth of ≈20 ks. Of these sources, 1130 are point-like sources detected with ≥3σ confidence in at least one of three energy bands (0.5-10, 0.5-2, and 2-10 keV), five have extended emission, and the remainder are detected at low significance. Since most sources have too few counts to permit individual classification, they are divided into five spectral groups defined by their quantile properties. We analyze stacked spectra of X-ray sources within each group, in conjunction with their fluxes, variability, and infrared counterparts, to identify the dominant populations in our survey. We find that ∼50% of our sources are foreground sources located within 1-2 kpc, which is consistent with expectations from previous surveys. Approximately 20% of sources are likely located in the proximity of the Scutum-Crux and near Norma arm, while 30% are more distant, in the proximity of the far Norma arm or beyond. We argue that a mixture of magnetic and nonmagnetic cataclysmic variables dominates the Scutum-Crux and near Norma arms, while intermediate polars and high-mass stars (isolated or in binaries) dominate the far Norma arm. We also present the cumulative number count distribution for sources in our survey that are detected in the hard energy band. A population of very hard sources in the vicinity of the far Norma arm and active galactic nuclei dominate the hard X-ray emission down to f{sub X} ≈ 10{sup –14} erg cm{sup –2} s{sup –1}, but the distribution curve flattens at fainter fluxes. We find good agreement between the observed distribution and predictions based on other surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Idakwo, Gabriel↗
DOE OSTI2014

The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS

Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ∼2600 EBs. When we require EB classification with ⩾90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

NEAR-INFRARED VARIABILITY AMONG YOUNG STELLAR OBJECTS IN THE STAR FORMATION REGION CYGNUS OB7

We present an analysis of near-infrared time-series photometry in J, H, and K bands for about 100 epochs of a 1 Degree-Sign Multiplication-Sign 1 Degree-Sign region of the Lynds 1003/1004 dark cloud in the Cygnus OB7 region. Augmented by data from the Wide-field Infrared Survey Explorer, we identify 96 candidate disk bearing young stellar objects (YSOs) in the region. Of these, 30 are clearly Class I or earlier. Using the Wide-Field Imaging Camera on the United Kingdom Infrared Telescope, we were able to obtain photometry over three observing seasons, with photometric uncertainty better than 0.05 mag down to J Almost-Equal-To 17. We study detailed light curves and color trajectories of {approx}50 of the YSOs in the monitored field. We investigate the variability and periodicity of the YSOs and find the data are consistent with all YSOs being variable in these wavelengths on timescales of a few years. We divide the variability into four observational classes: (1) stars with periodic variability stable over long timescales, (2) variables which exhibit short-lived cyclic behavior, (3) long-duration variables, and (4) stochastic variables. Some YSO variability defies simple classification. We can explain much of the observed variability as being due to dynamic and rotational changes in the disk, including an asymmetric or changing blocking fraction, changes to the inner disk hole size, as well as changes to the accretion rate. Overall, we find that the Class I:Class II ratio of the cluster is consistent with an age of <1 Myr, with at least one individual, wildly varying source {approx}100, 000 yr old. We have also discovered a Class II eclipsing binary system with a period of 17.87 days.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Deep Chandra X-Ray Study of Neutron Star Coalescence GW170817

We report Chandra observations of GW170817, the first neutron star–neutron star merger discovered by the joint LIGO-Virgo Collaboration, and the first direct detection of gravitational radiation associated with an electromagnetic counterpart, Fermi short γ-ray burst GRB 170817A. The event occurred on 2017 August 17 and subsequent observations identified an optical counterpart, SSS17a, coincident with NGC 4993 (∼10″ separation). Early Chandra (Δt∼2 days) and Swift (Δt∼1--3 days) observations yielded non-detections at the optical position, but ∼9 days post-trigger Chandra monitoring revealed an X-ray point source coincident with SSS17a. We present two deep Chandra observations totaling ∼95 ks, collected on 2017 September 01–02 (Δt∼15--16 days). We detect X-ray emission from SSS17a with L{sub 0.3--10keV}=2.6{sub −0.4}{sup +0.5}×10{sup 38} erg s{sup −1}, and a power law spectrum of Γ=2.4±0.8. We find that the X-ray light curve from a binary NS coalescence associated with this source is consistent with the afterglow from an off-axis short γ-ray burst, with a jet angled ≳23° from the line of sight. This event marks both the first electromagnetic counterpart to a LIGO-Virgo gravitational-wave source and the first identification of an off-axis short GRB. We also confirm extended X-ray emission from NGC 4993 (L{sub 0.3--10keV}∼9×10{sup 38} erg s{sup −1}) consistent with its E/S0 galaxy classification, and report two new Chandra point sources in this field, CXOU J130948 and CXOU J130946.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Wide cool and ultracool companions to nearby stars from Pan-STARRS 1

We present the discovery of 57 wide (>5'') separation, low-mass (stellar and substellar) companions to stars in the solar neighborhood identified from Pan-STARRS 1 (PS1) data and the spectral classification of 31 previously known companions. Our companions represent a selective subsample of promising candidates and span a range in spectral type of K7-L9 with the addition of one DA white dwarf. These were identified primarily from a dedicated common proper motion search around nearby stars, along with a few as serendipitous discoveries from our Pan-STARRS 1 brown dwarf search. Our discoveries include 23 new L dwarf companions and one known L dwarf not previously identified as a companion. The primary stars around which we searched for companions come from a list of bright stars with well-measured parallaxes and large proper motions from the Hipparcos catalog (8583 stars, mostly A-K dwarfs) and fainter stars from other proper motion catalogs (79170 stars, mostly M dwarfs). We examine the likelihood that our companions are chance alignments between unrelated stars and conclude that this is unlikely for the majority of the objects that we have followed-up spectroscopically. We also examine the entire population of ultracool (>M7) dwarf companions and conclude that while some are loosely bound, most are unlikely to be disrupted over the course of ∼10 Gyr. Our search increases the number of ultracool M dwarf companions wider than 300 AU by 88% and increases the number of L dwarf companions in the same separation range by 82%. Finally, we resolve our new L dwarf companion to HIP 6407 into a tight (0.''13, 7.4 AU) L1+T3 binary, making the system a hierarchical triple. Our search for these key benchmarks against which brown dwarf and exoplanet atmosphere models are tested has yielded the largest number of discoveries to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Classification of X-ray sources in the XMM-Newton serendipitous source catalog: Objects of special interest

We analyze 18 sources that showed interesting properties of periodicity, very soft spectra, and/or large long-term variability in X-rays in our project of classification of sources from the 2XMMi-DR3 catalog, but were poorly studied in the literature, in order to investigate their nature. Two hard sources show X-ray periodicities of ∼1.62 hr (2XMM J165334.4–414423) and ∼2.1 hr (2XMM J133135.2–315541) and are probably magnetic cataclysmic variables. One source, 2XMM J123103.2+110648, is an active galactic nucleus (AGN) candidate showing very soft X-ray spectra (kT ∼ 0.1 keV) and exhibiting an intermittent ∼3.8 hr quasi-periodic oscillation. There are six other very soft sources (with kT < 0.2 keV), which might be in other galaxies with luminosities between ∼10{sup 38}-10{sup 42} erg s{sup –1}. They probably represent a diverse group that might include objects such as ultrasoft AGNs and cool thermal disk emission from accreting intermediate-mass black holes. Six highly variable sources with harder spectra are probably in nearby galaxies with luminosities above 10{sup 37} erg s{sup –1} and thus are great candidates for extragalactic X-ray binaries. One of them (2XMMi J004211.2+410429, in M31) is probably a new-born persistent source, having been X-ray bright and hard in 0.3-10 keV for at least four years since it was discovered entering an outburst in 2007. Three highly variable hard sources appear at low galactic latitudes and have maximum luminosities below ∼10{sup 34} erg s{sup –1} if they are in our Galaxy. Thus, they are great candidates for cataclysmic variables or very faint X-ray transients harboring a black hole or neutron star. Our interpretations of these sources can be tested with future long-term X-ray monitoring and multi-wavelength observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine learning study of magnetism in uranium-based compounds

Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f electrons and include unconventional superconductivity and magnetism, hidden order, and heavy-fermion behavior. Due to the strongly correlated nature of the 5f electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput density functional theory simulations and experimental measurements for the families of uranium- and neptunium-based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation f-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the random forest regression algorithm. It can predict both spin and orbit moment size with root-mean-square error of 0.17 μ B and 0.19 μ B , respectively. Lastly, the random forest classification algorithm is used to predict the ordering (paramagnetic, ferromagnetic, and antiferromagnetic) of such systems with 76% accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using Machine Learning for Quantum Annealing Accuracy Prediction

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

STELLAR DIAMETERS AND TEMPERATURES. II. MAIN-SEQUENCE K- AND M-STARS

We present interferometric angular diameter measurements of 21 low-mass, K- and M-dwarfs made with the CHARA Array. This sample is enhanced by adding a collection of radius measurements published in the literature to form a total data set of 33 K-M-dwarfs with diameters measured to better than 5%. We use these data in combination with the Hipparcos parallax and new measurements of the star's bolometric flux to compute absolute luminosities, linear radii, and effective temperatures for the stars. We develop empirical relations for {approx}K0 to M4 main-sequence stars that link the stellar temperature, radius, and luminosity to the observed (B - V), (V - R), (V - I), (V - J), (V - H), and (V - K) broadband color index and stellar metallicity [Fe/H]. These relations are valid for metallicities ranging from [Fe/H] = -0.5 to +0.1 dex and are accurate to {approx}2%, {approx}5%, and {approx}4% for temperature, radius, and luminosity, respectively. Our results show that it is necessary to use metallicity-dependent transformations in order to properly convert colors into stellar temperatures, radii, and luminosities. Alternatively, we find no sensitivity to metallicity on relations we construct to the global properties of a star omitting color information, e.g., temperature-radius and temperature-luminosity. Thus, we are able to empirically quantify to what order the star's observed color index is impacted by the stellar iron abundance. In addition to the empirical relations, we also provide a representative look-up table via stellar spectral classifications using this collection of data. Robust examinations of single star temperatures and radii compared to evolutionary model predictions on the luminosity-temperature and luminosity-radius planes reveal that models overestimate the temperatures of stars with surface temperatures <5000 K by {approx}3%, and underestimate the radii of stars with radii <0.7 R{sub Sun} by {approx}5%. These conclusions additionally suggest that the models over account for the effects that the stellar metallicity may have on the astrophysical properties of an object. By comparing the interferometrically measured radii for the single star population to those of eclipsing binaries, we find that for a given mass, single and binary star radii are indistinguishable. However, we also find that for a given radius, the literature temperatures for binary stars are systematically lower compared to our interferometrically derived temperatures of single stars by {approx}200 to 300 K. The nature of this offset is dependent on the validation of binary star temperatures, where bringing all measurements to a uniform and correctly calibrated temperature scale is needed to identify any influence stellar activity may have on the physical properties of a star. Lastly, we present an empirically determined H-R diagram using fundamental properties presented here in combination with those in Boyajian et al. for a total of 74 nearby, main-sequence, A- to M-type stars, and define regions of habitability for the potential existence of sub-stellar mass companions in each system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A STUDY OF THE DIVERSE T DWARF POPULATION REVEALED BY WISE

We report the discovery of 87 new T dwarfs uncovered with the Wide-field Infrared Survey Explorer (WISE) and 3 brown dwarfs with extremely red near-infrared colors that exhibit characteristics of both L and T dwarfs. Two of the new T dwarfs are likely binaries with L7 {+-} 1 primaries and mid-type T secondaries. In addition, our follow-up program has confirmed 10 previously identified T dwarfs and 4 photometrically selected L and T dwarf candidates in the literature. This sample, along with the previous WISE discoveries, triples the number of known brown dwarfs with spectral types later than T5. Using the WISE All-Sky Source Catalog we present updated color-color and color-type diagrams for all the WISE-discovered T and Y dwarfs. Near-infrared spectra of the new discoveries are presented along with spectral classifications. To accommodate later T dwarfs we have modified the integrated flux method of determining spectral indices to instead use the median flux. Furthermore, a newly defined J-narrow index differentiates the early-type Y dwarfs from late-type T dwarfs based on the J-band continuum slope. The K/J indices for this expanded sample show that 32% of late-type T dwarfs have suppressed K-band flux and are blue relative to the spectral standards, while only 11% are redder than the standards. Comparison of the Y/J and K/J index to models suggests diverse atmospheric conditions and supports the possible re-emergence of clouds after the L/T transition. We also discuss peculiar brown dwarfs and candidates that were found not to be substellar, including two young stellar objects and two active galactic nuclei. The substantial increase in the number of known late-type T dwarfs provides a population that will be used to test models of cold atmospheres and star formation. The coolest WISE-discovered brown dwarfs are the closest of their type and will remain the only sample of their kind for many years to come.

79 ASTRONOMY AND ASTROPHYSICS↗