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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions is examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and datasets were examined. A penalized logistic regression model fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at different time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.

Bramer, L. M.↗
DOE OSTI2016

WE-H-BRA-07: Mechanistic Modelling of the Relative Biological Effectiveness of Heavy Charged Particles

Purpose Uncertainty in the Relative Biological Effectiveness (RBE) of heavy charged particles compared to photons remains one of the major uncertainties in particle therapy. As RBEs depend strongly on clinical variables such as tissue type, dose, and radiation quality, more accurate individualised models are needed to fully optimise treatments. MethodsWe have developed a model of DNA damage and repair following X-ray irradiation in a number of settings, incorporating mechanistic descriptions of DNA repair pathways, geometric effects on DNA repair, cell cycle effects and cell death. Our model has previously been shown to accurately predict a range of biological endpoints including chromosome aberrations, mutations, and cell death. This model was combined with nanodosimetric models of individual ion tracks to calculate the additional probability of lethal damage forming within a single track. These lethal damage probabilities can be used to predict survival and RBE for cells irradiated with ions of different Linear Energy Transfer (LET). ResultsBy combining the X-ray response model with nanodosimetry information, predictions of RBE can be made without cell-line specific fitting. The model’s RBE predictions were found to agree well with empirical proton RBE models (Mean absolute difference between models of 1.9% and 1.8% for cells with α/β ratios of 9 and 1.4, respectively, for LETs between 0 and 15 keV/µm). The model also accurately recovers the impact of high-LET carbon ion exposures, showing both the reduced efficacy of ions at extremely high LET, as well as the impact of defects in non-homologous end joining on RBE values in Chinese Hamster Ovary cells.ConclusionOur model is predicts RBE without the inclusion of empirical LET fitting parameters for a range of experimental conditions. This approach has the potential to deliver improved personalisation of particle therapy, with future developments allowing for the calculation of individualised RBEs. SJM is supported by a Marie Curie International Outgoing Fellowship from the European Commission’s FP7 program (EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Luminescence methodology to determine grain-boundary, grain-interior, and surface recombination in thin-film solar cells

We determine the grain-boundary (GB) recombination velocity, S GB , and grain-interior (GI) lifetime, t GI , parameters in superstrate CdS/CdTe thin-film solar cell technology by combining cathodoluminescence (CL) spectrum imaging and time-resolved photoluminescence (TRPL) measurements. We consider critical device formation stages, including after CdTe deposition, CdCl 2 treatment, and Cu diffusion. CL image analysis methods extract GB and GI intensities and grain size for hundreds of grains per sample. Concurrently, a three-dimensional CL model is developed to simulate the GI intensity as a function of t GI , S GB , grain size, and the surface recombination velocity, S surf . TRPL measurements provide an estimate of S surf for the CL model. A fit of GI intensity vs. grain size data with the CL model gives a self-consistent and representative set of S GB and t GI values for the samples: S GB (t GI ) = 2.6 x 10 6 cm/s (68-250 ps), S GB (t GI ) = 4.1 x 10 5 cm/s (1.5-3.3 ns), and S GB (t GI ) = 5.5 x 10 5 cm/s (1.0-3.8 ns) for as-deposited, CdCl 2 -treated, and CdCl 2- A nd Cu-treated samples, respectively. Thus, we find that the CdCl2 treatment both helps to passivate GBs and significantly increase the GI lifetime. Subsequent Cu diffusion increases GB recombination slightly and has nuanced effects on the GI lifetime. Finally, as a partial check on the S GB and t GI values, they are input to a Sentaurus device model, and the simulated performance is compared to the measured performance. The methodology developed here can be applied broadly to CdTe and CdSeTe thin-film technology and to other thin-film solar cell materials including Cu(In 1-x Ga x )Se 2 , Cu 2 ZnSnS 4 , and perovskites.

14 SOLAR ENERGY↗
DOE OSTI2014

CHARA/MIRC observations of two M supergiants in Perseus OB1: Temperature, bayesian modeling, and compressed sensing imaging

Two red supergiants (RSGs) of the Per OB1 association, RS Per and T Per, have been observed in the H band using the Michigan Infra-Red Combiner (MIRC) instrument at the CHARA array. The data show clear evidence of a departure from circular symmetry. We present here new techniques specially developed to analyze such cases, based on state-of-the-art statistical frameworks. The stellar surfaces are first modeled as limb-darkened disks based on SATLAS models that fit both MIRC interferometric data and publicly available spectrophotometric data. Bayesian model selection is then used to determine the most probable number of spots. The effective surface temperatures are also determined and give further support to the recently derived hotter temperature scales of RSGs. The stellar surfaces are reconstructed by our model-independent imaging code SQUEEZE, making use of its novel regularizer based on Compressed Sensing theory. We find excellent agreement between the model-selection results and the reconstructions. Our results provide evidence for the presence of near-infrared spots representing about 3%-5% of the stellar flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

electric vehicle, multi-unit dwelling, charging in↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Explosion Source Models and the Scattering Origin of Regional Phases from SPE Phase 1 Coda Spectral Ratios

The Source Physics Experiment(SPE)explosions provide comprehensive ground truth information,and nearby recordings that are rarely available in typical monitoring scenarios. Here, we look for relationships with local and regional distance data to understand source and propagation effects in an effort to improve monitoring at a distance. We examine source spectral ratios, which can be measured to high precision by taking advantage of many SPE and independently operated stations, as well as the redundancy available from coda waves. Classical source models (Mueller and Murphy, 1971, MM71; Denny and Johnson, 1991, DJ91)predict the source ratios poorly; however, a hybrid model(MM71 with DJ91 cavity radii)performs better, and by varying cavity and elastic radii we obtain models that fit to within measurement precision. Based on this, the best prediction model for the SPE suite is the hybrid model with cavity radius decreased by 95%, and elastic radius decreased by 75%. This works best for mid-range scaled depths of burial (SPE-1, 2, 3, and 5). We do not guarantee transportability to other geologies,depth and scaled depth ranges. Finally, we observe a distinct spectral modulation at 6-9Hzthat is not predicted by classical models, and is most likely the result of interference of short period surface waves(Rg)with those produced by spallation of near surface layers.The same modulationis observed for compressional (P) and shear(S) waves at distance, indicating the origin of these waves as scattered Rg.The scattering process must be accounted for quantitatively when using distant signals to monitor treaty compliance over broad regions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Universality Classes of Interaction Structures for NK Fitness Landscapes

Kauffman’s NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k≤L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

HZ Her: Stellar radius from X-ray eclipse observations, evolutionary state, and a new distance

Observations of HZ Her/Her X-1 by the Rossi X-Ray Timing Explorer (RXTE) covering high state eclipses of the neutron star are analyzed here. Models of the eclipse are used to measure the radius and atmospheric scale height of HZ Her, the stellar companion to the neutron star. The radius is 2.58-3.01 × 10{sup 11} cm, depending on system inclination and mass ratio (q), with an accuracy of ∼1 part in 1000 for given inclination and q. We fit Kurucz model stellar atmosphere models to archival optical observations. The resulting effective temperature (T {sub eff}) of the unheated face of HZ Her is determined to be in the 2σ range of 7720 K-7865 K, and metallicity (log (Z/Z {sub ☉})) in the range of –0.27 to +.03. The model atmosphere surface flux and new radius yield a new distance to HZ Her/Her X-1, depending on system inclination and q: a best-fit value of 6.1 kpc with upper and lower limits of 5.7 kpc and 7.0 kpc. We calculate stellar evolution models for the range of allowed masses (from orbital parameters) and allowed metallicities (from optical spectrum fits). The stellar models agree with T {sub eff} and the radius of HZ Her for two narrow ranges of mass: 2.15-2.20 M {sub ☉} and 2.35-2.45 M {sub ☉}. This lower mass range implies a low neutron star mass (1.3 M {sub ☉}), whereas the higher mass range implies a high neutron star mass (1.5-1.7 M {sub ☉}).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Blind Modeling Validation Exercises Using the Horizontal Dry Cask Simulator

The U.S. Department of Energy (DOE) established a need to understand the thermal-hydraulic properties of dry storage systems for commercial spent nuclear fuel (SNF) in response to a shift towards the storage of high-burnup (HBU) fuel (> 45 gigawatt days per metric ton of uranium, or GWd/MTU). This shift raises concerns regarding cladding integrity, which faces increased risk at the higher temperatures within spent fuel assemblies present within HBU fuel compared to low-burnup fuel (≤ 45 GWd/MTU). A dry cask simulator (DCS) was built at Sandia National Laboratories (SNL) in Albuquerque, New Mexico to produce validation-quality data that can be used to test the accuracy of the modeling used to predict cladding temperatures. These temperatures are critical to evaluating cladding integrity throughout the storage cycle of commercial spent nuclear fuel. A model validation exercise was previously carried out for the DCS in a vertical configuration. Lessons learned during the previous validation exercise have been applied to a new, blind study using a horizontal dry cask simulator (HDCS). Three modeling institutions – the Nuclear Regulatory Commission (NRC), Pacific Northwest National Laboratory (PNNL), and Empresa Nacional del Uranio, S.A., S.M.E. (ENUSA) – were granted access to the input parameters from the DCS Handbook, SAND2017-13058R, and results from a limited data set from the horizontal BWR dry cask simulator tests reported in the HDCS update report, SAND2019-11688R. With this information, each institution was tasked to calculate peak cladding temperatures and air mass flow rates for ten HDCS test cases. Axial as well as vertical and horizontal transverse temperature profiles were also calculated. These calculations were done using modeling codes (ANSYS/Fluent, STAR-CCM+, or COBRA-SFS), each with their own unique combination of modeling assumptions and boundary conditions. For this validation study, the ten test cases of the horizontal dry cask simulator were defined by three independent variables – fuel assembly decay heat (0.5 kW, 1 kW, 2.5 W, and 5 kW), internal backfill pressure (100 kPa and 800 kPa), and backfill gas (helium and air). The plots provided in Chapter 3 of this report show the axial, vertical, and horizontal temperature profiles obtained from the dry cask simulator experiments in the horizontal configuration and the corresponding models used to describe the thermal-hydraulic behavior of this system. The tables provided in Chapter 3 illustrate the closeness of fit of the model data to the experiment data through root mean square (RMS) calculations of the error in peak cladding temperatures (PCTs), PCT axial locations, axial temperature profiles, vertical and horizontal temperature profiles at two different axial locations, and air mass flow rates for the ten test cases, normalized by the experimental results. The model results are assigned arbitrary model numbers to retain anonymity. Due to the relatively flat axial temperature profiles, small temperature gradients resulted in large deviations of all models’ PCT axial location from the experimental PCT axial location. When the PCT axial location error is excluded in the calculation of the combined RMS of the normalized errors that considers PCT, the temperature profiles, and the air mass flow rates, the model data fits the experimental data to within 5%. When the vault information is excluded, the model data fits the experimental data to within 2.5%. An error analysis was developed further for one model, using the model and experimental uncertainties in each validation parameter to calculate validation uncertainties. The uncertainties for each parameter were used to define quantifiable validation criteria. For this analysis, the model was considered validated for a given comparison metric if the normalized error in that metric divided by the validation uncertainty was less than or equal to 1. When considering the combined RMS of the normalized errors of all metrics divided by their validation uncertainties, the model was found to have satisfied the criterion for model validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Adsorption Studies of Dimetridazole and Metronidazole onto Biochar Derived from Sugarcane Bagasse: Kinetic, Equilibrium, and Mechanisms

Biochar derived from sugarcane bagasse was prepared at different pyrolysis temperatures and holding times for adsorption of dimetridazole and metronidazole from aqueous solution. The optimal pyrolysis temperature and holding time were 500 °C and 120 min, respectively. The removal efficiencies of dimetridazole and metronidazole were 98.0% and 88.8%, respectively, at biochar dosage of 4.0 g L{sup −1} and solution temperature of 30 °C. Kinetics data were fitted by four kinetics models, and results indicated that adsorption was governed by pseudo-second-order kinetics. Adsorption is a multi-step process that involves film diffusion and pore filling. In addition, five isotherm models were employed to describe the adsorption equilibrium. Brunauer–Emmett–Teller model presented better fitting for the adsorption equilibrium, and the maximum adsorption capacities were 72.17 and 23.61 mg g{sup − 1} for dimetridazole and metronidazole, respectively. The calculated values of ΔG{sup 0} and ΔH{sup 0} indicated the non-spontaneous and exothermic nature of the adsorption process at the range of temperature studied. Thermodynamic studies also revealed that physical and chemical adsorptions were co-action. The adsorption mechanisms of dimetridazole and metronidazole onto biochar were mainly hydrogen bonding and π–π interaction. All the results revealed that sugarcane bagasse biochar can be used as alternative to costly adsorbents for the removal of dimetridazole and metronidazole from aqueous solution.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Kinetic model for quartz and spinel dissolution during melting of high-level-waste glass batch

The dissolution of quartz particles and the growth and dissolution of crystalline phases during the conversion of batch to glass potentially affects both the glass melting process and product quality. Crystals of spinel exiting the cold cap to molten glass below can be troublesome during the vitrification of iron-containing high-level wastes. To estimate the distribution of quartz and spinel fractions within the cold cap, we used kinetic models that relate fractions of these phases to temperature and heating rate. Fitting the model equations to data showed that the heating rate, apart from affecting quartz and spinel behavior directly, also affects them indirectly via concurrent processes, such as the formation and motion of bubbles. Because of these indirect effects, it was necessary to allow one kinetic parameter (the pre-exponential factor) to vary with the heating rate. The resulting kinetic equations are sufficiently simple for the detailed modeling of batch-to-glass conversion as it occurs in glass melters. The estimated fractions and sizes of quartz and spinel particles as they leave the cold cap, determined in this study, will provide the source terms needed for modeling the behavior of these solid particles within the flow of molten glass in the melter.

Batch melting, Cold cap, Quartz, Spinel, Waste gla↗
DOE OSTI2022

The dark matter halo masses of elliptical galaxies as a function of observationally robust quantities

Context. The assembly history of the stellar component of a massive elliptical galaxy is closely related to that of its dark matter halo. Measuring how the properties of galaxies correlate with their halo mass can therefore help to understand their evolution. Aims. We investigate how the dark matter halo mass of elliptical galaxies varies as a function of their properties, using weak gravitational lensing observations. To minimise the chances of biases, we focus on the following galaxy properties that can be determined robustly: the surface brightness profile and the colour. Methods. We selected 2409 central massive elliptical galaxies (log M*/M ⊙ ≳ 11.4) from the Sloan Digital Sky Survey spectroscopic sample. We first measured their surface brightness profile and colours by fitting Sérsic models to photometric data from the Kilo-Degree Survey (KiDS). We fitted their halo mass distribution as a function of redshift, rest-frame r-band luminosity, half-light radius, and rest-frame u - g colour, using KiDS weak lensing measurements and a Bayesian hierarchical approach. For the sake of robustness with respect to assumptions on the large-radii behaviour of the surface brightness, we repeated the analysis replacing the total luminosity and half-light radius with the luminosity within a 10 kpc aperture, L r, 10 , and the light-weighted surface brightness slope, Γ 10 . Results. We did not detect any correlation between the halo mass and either the half-light radius or colour at fixed redshift and luminosity. Using the robust surface brightness parameterisation, we found that the halo mass correlates weakly with L r,10 and anti-correlates with Γ 10 . At fixed redshift, L r, 10 and Γ 10 , the difference in the average halo mass between galaxies at the 84th percentile and 16th percentile of the colour distribution is 0.00 ± 0.11 dex. Conclusion. Our results indicate that the average star formation efficiency of massive elliptical galaxies has little dependence on their final size or colour. This suggests that the origin of the diversity in the size and colour distribution of these objects lies with properties other than the halo mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

High-frequency characterization of thermionic charge transport in silicon-on-insulator nanowire transistors

We report on DC and microwave electrical transport measurements in silicon-on-insulator nano-transistors at low and room temperature. At low source-drain voltage, the DC current and radio frequency response show signs of conductance quantization. We attribute this to Coulomb blockade resulting from barriers formed at the spacer-gate interfaces. We show that at high bias transport occurs thermionically over the highest barrier: Transconductance traces obtained from microwave scattering-parameter measurements at liquid helium and room temperature are accurately fitted by a thermionic model. From the fits we deduce the ratio of gate capacitance and quantum capacitance, as well as the electron temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Radial velocity variations of photometrically quiet, chromospherically inactive Kepler stars: A link between RV jitter and photometric flicker

We compare stellar photometric variability, as measured from Kepler light curves by Basri et al., with measurements of radial velocity (RV) rms variations of all California Planet Search overlap stars. We newly derive rotation periods from the Kepler light curves for all of the stars in our study sample. The RV variations reported herein range from less than 4 to 135 m s{sup –1}, yet the stars all have amplitudes of photometric variability less than 3 mmag, reflecting the preference of the RV program for chromospherically 'quiet' stars. Despite the small size of our sample, we find with high statistical significance that the RV rms manifests strongly in the Fourier power spectrum of the light curve: stars that are noisier in RV have a greater number of frequency components in the light curve. We also find that spot models of the observed light curves systematically underpredict the observed RV variations by factors of ∼2-1000, likely because the low-level photometric variations in our sample are driven by processes not included in simple spot models. The stars best fit by these models tend to have simpler light curves, dominated by a single relatively high-amplitude component of variability. Finally, we demonstrate that the RV rms behavior of our sample can be explained in the context of the photometric variability evolutionary diagram introduced by Bastien et al. We use this diagram to derive the surface gravities of the stars in our sample, revealing many of them to have moved off the main sequence. More generally, we find that the stars with the largest RV rms are those that have evolved onto the 'flicker floor' sequence in that diagram, characterized by relatively low amplitude but highly complex photometric variations which grow as the stars evolve to become subgiants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Global study of the simplest scalar phantom dark matter model

We present a global study of the simplest scalar phantom dark matter model. The best fit parameters of the model are determined by simultaneously imposing (i) relic density constraint from WMAP, (ii) 225 live days data from direct experiment XENON100, (iii) upper limit of gamma-ray flux from Fermi-LAT indirect detection based on dwarf spheroidal satellite galaxies, and (iv) the Higgs boson candidate with a mass about 125 GeV and its invisible branching ratio no larger than 40% if the decay of the Higgs boson into a pair of dark matter is kinematically allowed. The allowed parameter space is then used to predict annihilation cross sections for gamma-ray lines, event rates for three processes mono-b jet, single charged lepton and two charged leptons plus missing energies at the Large Hadron Collider, as well as to evaluate the muon anomalous magnetic dipole moment for the model.

79 ASTRONOMY AND ASTROPHYSICS↗