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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 OSTI2014

HIghMass-high H I mass, H I-rich galaxies at z ∼ 0 sample definition, optical and Hα imaging, and star formation properties

We present first results of the study of a set of exceptional H I sources identified in the 40% ALFALFA extragalactic H I survey catalog α.40 as both being H I massive (M{sub HI}>10{sup 10} M{sub ⊙}) and having high gas fractions for their stellar masses: the HIghMass galaxy sample. We analyze UV- and optical-broadband and Hα images to understand the nature of their relatively underluminous disks in optical and to test whether their high gas fractions can be tracked to higher dark matter halo spin parameters or late gas accretion. Estimates of their star formation rates (SFRs) based on spectral energy distribution fitting agree within uncertainties with the Hα luminosity inferred current massive SFRs. The H II region luminosity functions, parameterized as dN/dlog L∝L {sup α}, have standard slopes at the luminous end (α ∼ –1). The global SFRs demonstrate that the HIghMass galaxies exhibit active ongoing star formation (SF) with moderate SF efficiency but, relative to normal spirals, a lower integrated SFR in the past. Because the SF activity in these systems is spread throughout their extended disks, they have overall lower SFR surface densities and lower surface brightness in the optical bands. Relative to normal disk galaxies, the majority of HIghMass galaxies have higher Hα equivalent widths and are bluer in their outer disks, implying an inside-out disk growth scenario. Downbending double exponential disks are more frequent than upbending disks among the gas-rich galaxies, suggesting that SF thresholds exist in the downbending disks, probably as a result of concentrated gas distribution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Recovery time in quantum dynamics of wave packets

A wave packet formed by a linear superposition of bound states with an arbitrary energy spectrum returns arbitrarily close to the initial state after a quite long time. A method in which quantum recovery times are calculated exactly is developed. In particular, an exact analytic expression is derived for the recovery time in the limiting case of a two-level system. In the general case, the reciprocal recovery time is proportional to the Gauss distribution that depends on two parameters (mean value and variance of the return probability). The dependence of the recovery time on the mean excitation level of the system is established. The recovery time is the longest for the maximal excitation level.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Density estimation via measure transport: Outlook for applications in the biological sciences

Abstract One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

gene expression data↗
DOE OSTI2021

A Privacy-Preserving Distributed Control of Optimal Power Flow

Here, we consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Optical/near-infrared polarization survey of Sh 2-29: Magnetic fields, dense cloud fragmentations, and anomalous dust grain sizes

Sh 2-29 is a conspicuous star-forming region marked by the presence of massive embedded stars as well as several notable interstellar structures. In this research, our goals were to determine the role of magnetic fields and to study the size distribution of interstellar dust particles within this turbulent environment. We have used a set of optical and near-infrared polarimetric data obtained at OPD/LNA (Brazil) and CTIO (Chile), correlated with extinction maps, Two Micron All Sky Survey data, and images from the Digitized Sky Survey and Spitzer. The region's most striking feature is a swept out interstellar cavity whose polarimetric maps indicate that magnetic field lines were dragged outward, piling up along its borders. This led to a higher magnetic strength value (≈400 μG) and an abrupt increase in polarization degree, probably due to an enhancement in alignment efficiency. Furthermore, dense cloud fragmentations with peak A{sub V} between 20 and 37 mag were probably triggered by its expansion. The presence of 24 μm point-like sources indicates possible newborn stars inside this dense environment. A statistical analysis of the angular dispersion function revealed areas where field lines are aligned in a well-ordered pattern, seemingly due to compression effects from the H II region expansion. Finally, Serkowski function fits were used to study the ratio of the total-to-selective extinction, revealing a dual population of anomalous grain particle sizes. This trend suggests that both effects of coagulation and fragmentation of interstellar grains are present in the region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2014

Assessment of interpatient heterogeneity in tumor radiosensitivity for nonsmall cell lung cancer using tumor-volume variation data

Purpose: In our previous work, the authors showed that a distribution of cell surviving fractionsS{sub 2} in a heterogeneous group of patients could be derived from tumor-volume variation curves during radiotherapy for head and neck cancer. In this research study, the authors show that this algorithm can be applied to other tumors, specifically in nonsmall cell lung cancer. This new application includes larger patient volumes and includes comparison of data sets obtained at independent institutions. Methods: Our analysis was based on two data sets of tumor-volume variation curves for heterogeneous groups of 17 patients treated for nonsmall cell lung cancer with conventional dose fractionation. The data sets were obtained previously at two independent institutions by using megavoltage computed tomography. Statistical distributions of cell surviving fractionsS{sub 2} and clearance half-lives of lethally damaged cells T{sub 1/2} have been reconstructed in each patient group by using a version of the two-level cell population model of tumor response and a simulated annealing algorithm. The reconstructed statistical distributions of the cell surviving fractions have been compared to the distributions measured using predictive assays in vitro. Results: Nonsmall cell lung cancer presents certain difficulties for modeling surviving fractions using tumor-volume variation curves because of relatively large fractional hypoxic volume, low gradient of tumor-volume response, and possible uncertainties due to breathing motion. Despite these difficulties, cell surviving fractionsS{sub 2} for nonsmall cell lung cancer derived from tumor-volume variation measured at different institutions have similar probability density functions (PDFs) with mean values of 0.30 and 0.43 and standard deviations of 0.13 and 0.18, respectively. The PDFs for cell surviving fractions S{sub 2} reconstructed from tumor volume variation agree with the PDF measured in vitro. Conclusions: The data obtained in this work, when taken together with the data obtained previously for head and neck cancer, suggests that the cell surviving fractionsS{sub 2} can be reconstructed from the tumor volume variation curves measured during radiotherapy with conventional fractionation. The proposed method can be used for treatment evaluation and adaptation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Correlated prompt fission data in transport simulations

Detailed information on the fission process can be inferred from the observation, modeling and theoretical understanding of prompt fission neutron and γ-ray observables. Beyond simple average quantities, the study of distributions and correlations in prompt data, e.g., multiplicity-dependent neutron and γ-ray spectra, angular distributions of the emitted particles, n -n, n - γ, and γ - γ correlations, can place stringent constraints on fission models and parameters that would otherwise be free to be tuned separately to represent individual fission observables. The FREYA and CGMF codes have been developed to follow the sequential emissions of prompt neutrons and γ rays from the initial excited fission fragments produced right after scission. Both codes implement Monte Carlo techniques to sample initial fission fragment configurations in mass, charge and kinetic energy and sample probabilities of neutron and γ emission at each stage of the decay. This approach naturally leads to using simple but powerful statistical techniques to infer distributions and correlations among many observables and model parameters. The comparison of model calculations with experimental data provides a rich arena for testing various nuclear physics models such as those related to the nuclear structure and level densities of neutron-rich nuclei, the γ-ray strength functions of dipole and quadrupole transitions, the mechanism for dividing the excitation energy between the two nascent fragments near scission, and the mechanisms behind the production of angular momentum in the fragments, etc. Beyond the obvious interest from a fundamental physics point of view, such studies are also important for addressing data needs in various nuclear applications. The inclusion of the FREYA and CGMF codes into the MCNP6.2 and MCNPX - PoliMi transport codes, for instance, provides a new and powerful tool to simulate correlated fission events in neutron transport calculations important in nonproliferation, safeguards, nuclear energy, and defense programs. Here, this review provides an overview of the topic, starting from theoretical considerations of the fission process, with a focus on correlated signatures. It then explores the status of experimental correlated fission data and current efforts to address some of the known shortcomings. Numerical simulations employing the FREYA and CGMF codes are compared to experimental data for a wide range of correlated fission quantities. The inclusion of those codes into the MCNP6.2 and MCNPX - PoliMi transport codes is described and discussed in the context of relevant applications. The accuracy of the model predictions and their sensitivity to model assumptions and input parameters are discussed. Lastly, a series of important experimental and theoretical questions that remain unanswered are presented, suggesting a renewed effort to address these shortcomings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

RADIAL EVOLUTION OF THE WAVEVECTOR ANISOTROPY OF SOLAR WIND TURBULENCE BETWEEN 0.3 AND 1 AU

We present observations of the power spectral anisotropy in the wavevector space of solar wind turbulence and study how it evolves in interplanetary space with increasing heliocentric distance. We use magnetic field measurements from the Helios 2 spacecraft within 1 AU. To derive the power spectral density (PSD) in the (k{sub Parallel-To }, k ) space based on single-satellite measurements is a challenging task that had not been accomplished previously. Here, we derive the spectrum PSD{sub 2D}(k{sub Parallel-To }, k{sub Up-Tack }) from the spatial correlation function CF{sub 2D}(r{sub Parallel-To }, r ) by a transformation according to the projection-slice theorem. We find the so-constructed PSDs to be distributed in k space mainly along a ridge that is more inclined toward the k{sub Up-Tack} axis than the k{sub Parallel-To} axis. Furthermore, this ridge of the distribution is found to gradually get closer to the k{sub Up-Tack} axis as the outer scale length of the turbulence becomes larger with increasing radial distance. In the vicinity of the k{sub Parallel-To} axis, a minor spectral component appears that probably corresponds to quasi-parallel Alfvenic fluctuations. Their relative contribution to the total spectral density tends to decrease with radial distance. These findings suggest that solar wind turbulence undergoes an anisotropic cascade transporting most of its magnetic energy toward larger k{sub Up-Tack} and that the anisotropy in the inertial range is radially developing further at scales that are relatively far from the ever increasing outer scale. For the ion-scale fluctuations, we speculate, from the radial evolution of the extended oblique major component, a transition tendency from dominance by oblique Alfven/ion-cyclotron waves (<1 AU) to dominance by kinetic Alfven waves (>1 AU)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Evaluating design safety margins in the American Society of Mechanical Engineers graphite core components design-by-analysis assessments

Graphite is an important material being used for core components in next-generation high-temperature gas-cooled nuclear reactors. The selection of graphite grade for a specific Designer is a complex task, dependent on reactor conditions, component functionality, and required reliability. The American Society of Mechanical Engineers (ASME) provides two semi-probabilistic design-by-analysis assessments to evaluate graphite core components against design reliability targets. The simplified assessment uses a 2-parameter Weibull distribution to describe the graphite grade’s tensile-strength distribution to establish component stress limits. The full assessment uses the 3-parameter Weibull distribution and a modified Weakest-Link Theory approach to calculate a component design probability of failure. The paper defines recommended assessment rules, which are the as-written simplified assessment and the full assessment with parameter lower bounds, the modulus update with threshold reduction, and the 2027 grouping rules. Code rules are applied to three grades: 2114, IG-110, and NBG-18. The baseline margin calculation is developed using the experimental tensile dogbone specimen. Percent margin is defined as the percent reduction in the median experimental load to obtain the allowable load per ASME assessments. Under the recommended rules, the SRC–1 margin in the simplified assessment ranged from 40.2 % to 52.7 % among the grades in this study and from 36.1 % to 49.8 % in the full assessment. The full assessment only decreases the margins by 2.5–4.5 % for the SRC-1 components and 0–1.5 % for the SRC-2 components for this baseline case. Margin is inversely related to material median strength (i.e., the strongest grade, 2114, has the lowest margin).

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2011

SOURCE CONTAMINATION IN X-RAY STUDIES OF STAR-FORMING REGIONS: APPLICATION TO THE CHANDRA CARINA COMPLEX PROJECT

We describe detailed simulations of X-ray-emitting populations to evaluate the levels of contamination by both Galactic and extragalactic X-ray sources unrelated to a star-forming region under study. For Galactic contaminations, we consider contribution from main-sequence stars and giants (not including cataclysmic variables and other classes of accretion-driven X-ray binary systems), as they make the dominant contribution at the position of the Carina Nebula. The simulations take into consideration a variety of technical factors involving a Galactic population synthesis model, stellar X-ray luminosity functions, Chandra telescope response, source detection methodology, and possible spatial variations in the X-ray background and absorption through molecular clouds. When applied to the 1.42 deg{sup 2} field of the Chandra Carina Complex Project (CCCP), the simulations predict {approx}5000 contaminating sources (1 source arcmin{sup -2} of the survey), evenly distributed across the field. The results of the simulations are further employed in a companion CCCP study to assign membership probabilities to individual sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Likelihood non-Gaussianity in large-scale structure analyses

The standard present-day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference – that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. power spectrum multipole ($P_ℓ$) analysis and the Sinha et al. group multiplicity function ($ζ$) analysis. Using non-parametric divergence estimators on mock catalogues originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the $P_ℓ$ and $ζ$ likelihoods. We then use Gaussian mixture density estimation and independent component analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian pseudo-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. and Sinha et al. parameters. Likelihood non-Gaussianity shifts the fσ8 constraint by -0.44σ, but otherwise does not significantly impact the overall parameter constraints of Beutler et al. For the $ζ$ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of the Sinha et al. halo occupation parameters. For log $M_1$ and α, the posteriors are shifted by +0.43σ and -0.51σ and broadened by 42 per cent and 66 per cent, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A cloud computing approach to massively-parallel distributed stochastic optimization with application to geomechanical reservoir characterization

Many search strategies used to optimize parameters during hydrologic model calibration are inherently limited in that simulations must be performed sequentially in order for the algorithm to converge. As a result, the overall time required to reach a solution increases linearly with the number of steps necessary. In contrast, sampling strategies that attempt to approximate the posterior distribution of the parameters do not require the model simulations to be run in sequence and therefore invite the use of a massively parallel approach across heterogeneous computing environments. We apply a cloud computing methodology to this problem where a head node running on Amazon Web Services continuously produces candidate models that are distributed to arbitrary compute environments to run the simulation, in our case Clemson’s Palmetto cluster computer. Thus the time required to obtain a sufficient number of samples to produce an adequate approximation of the posterior distribution of the parameters is limited by the number of available compute sites. Monte Carlo sampling of the posterior space would be duplicative and inefficient, thus we combine Latin hypercube methods with genetic algorithms to ensure that no large regions of the parameter space are left unexplored, and that high probability regions are investigated thoroughly. We independently assess the fit to different types of data sets without the need to construct a single objective function. Sets of Pareto optimal models are particularly valuable when the quality and information content of data is poorly known or conceptual model errors exist in our simulation. We illustrate our approach for a problem where in situ strain data are used to calibrate the parameters of an oil reservoir in the Avant oil field in Oklahoma. We use well injection and shut-in to stress a deep (500m) confined formation, and observe the geomechanical response using borehole strainmeters installed in the shallow subsurface (30m). These data were then used to calibrate the poroelastic properties of the reservoir and caprock along with the geometry of an elliptic channel associated with the formation. Using our approach we are able to identify issues with model error as well as substantial tradeoffs that occur within the parameters.

inversion, stochastic, geomechanics, poroelastic, ↗
DOE OSTI2016

Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS)

Abstract. Microphysical processes, such as the formation, growth, and evaporation of precipitation, interact with variability and covariances (e.g., fluxes) in moisture and heat content. For instance, evaporation of rain may produce cold pools, which in turn may trigger fresh convection and precipitation. These effects are usually omitted or else crudely parameterized at subgrid scales in weather and climate models.A more formal approach is pursued here, based on predictive, horizontally averaged equations for the variances, covariances, and fluxes of moisture and heat content. These higher-order moment equations contain microphysical source terms. The microphysics terms can be integrated analytically, given a suitably simple warm-rain microphysics scheme and an approximate assumption about the multivariate distribution of cloud-related and precipitation-related variables. Performing the integrations provides exact expressions within an idealized context.A large-eddy simulation (LES) of a shallow precipitating cumulus case is performed here, and it indicates that the microphysical effects on (co)variances and fluxes can be large. In some budgets and altitude ranges, they are dominant terms. The analytic expressions for the integrals are implemented in a single-column, higher-order closure model. Interactive single-column simulations agree qualitatively with the LES. The analytic integrations form a parameterization of microphysical effects in their own right, and they also serve as benchmark solutions that can be compared to non-analytic integration methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Generalized ensemble method applied to study systems with strong first order transitions

At strong first-order phase transitions, the entropy versus energy or, at constant pressure, enthalpy, exhibits convex behavior, and the statistical temperature curve correspondingly exhibits an S-loop or back-bending. In the canonical and isothermal-isobaric ensembles, with temperature as the control variable, the probability density functions become bimodal with peaks localized outside of the S-loop region. Inside, states are unstable, and as a result simulation of equilibrium phase coexistence becomes impossible. To overcome this problem, a method was proposed by Kim, Keyes and Straub, where optimally designed generalized ensemble sampling was combined with replica exchange, and denoted generalized replica exchange method (gREM). This new technique uses parametrized effective sampling weights that lead to a unimodal energy distribution, transforming unstable states into stable ones. In the present study, the gREM, originally developed as a Monte Carlo algorithm, was implemented to work with molecular dynamics in an isobaric ensemble and coded into LAMMPS, a highly optimized open source molecular simulation package. Lastly, the method is illustrated in a study of the very strong solid/liquid transition in water.

36 MATERIALS SCIENCE↗
DOE OSTI2011

RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences

Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article, we present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field. We show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions. Results: Our analysis of three publically available datasets showed a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families showed that the RNAG sampled structures were generally compact around their ensemble centroids, and at least 11 families had at least two well-separated clusters of predicted structures. In general, the distance between a reference structure and our predicted structure was large relative to the variation among structures within an ensemble. Availability: The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbweb.ccv.brown.edu/rnag.html

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

MODELING THE ANOMALY OF SURFACE NUMBER DENSITIES OF GALAXIES ON THE GALACTIC EXTINCTION MAP DUE TO THEIR FIR EMISSION CONTAMINATION

The most widely used Galactic extinction map is constructed assuming that the observed far-infrared (FIR) fluxes come entirely from Galactic dust. According to the earlier suggestion by Yahata et al., we consider how FIR emission of galaxies affects the SFD map. We first compute the surface number density of Sloan Digital Sky Survey (SDSS) DR7 galaxies as a function of the r-band extinction, A {sub r,} {sub SFD}. We confirm that the surface densities of those galaxies positively correlate with A {sub r,} {sub SFD} for A {sub r,} {sub SFD} < 0.1, as first discovered by Yahata et al. for SDSS DR4 galaxies. Next we construct an analytical model to compute the surface density of galaxies, taking into account the contamination of their FIR emission. We adopt a log-normal probability distribution for the ratio of 100 μm and r-band luminosities of each galaxy, y ≡ (νL){sub 100} {sub μm}/(νL) {sub r}. Then we search for the mean and rms values of y that fit the observed anomaly, using the analytical model. The required values to reproduce the anomaly are roughly consistent with those measured from the stacking analysis of SDSS galaxies. Due to the limitation of our statistical modeling, we are not yet able to remove the FIR contamination of galaxies from the extinction map. Nevertheless, the agreement with the model prediction suggests that the FIR emission of galaxies is mainly responsible for the observed anomaly. Whereas the corresponding systematic error in the Galactic extinction map is 0.1-1 mmag, it is directly correlated with galaxy clustering and thus needs to be carefully examined in precision cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SHELS: Complete Redshift surveys of two widely separated fields

The Smithsonian Hectospec Lensing Survey (SHELS) is a complete redshift survey covering two well-separated fields (F1 and F2) of the Deep Lens Survey (DLS). Both fields are more than 94% complete to a Galactic extinction corrected R {sub 0} = 20.2. Here, we describe the redshift survey of the F1 field centered at R.A.{sub 2000} = 00{sup h}53{sup m}25.ˢ3 and decl.{sub 2000} = 12°33′55″; like F2, the F1 field covers ∼4 deg{sup 2}. The redshift survey of the F1 field includes 9426 new galaxy redshifts measured with Hectospec on the MMT (published here). As a guide to future uses of the combined survey, we compare the mass metallicity relation and the distributions of D {sub n}4000 as a function of stellar mass and redshift for the two fields. The mass–metallicity relations differ by an insignificant 1.6σ. For galaxies in the stellar mass range 10{sup 10}–10{sup 11} M {sub ⊙}, the increase in the star-forming fraction with redshift is remarkably similar in the two fields. The seemingly surprising 31%–38% difference in the overall galaxy counts in F1 and F2 is probably consistent with the expected cosmic variance given the subtleties of the relative systematics in the two surveys. We also review the DLS cluster detections in the two fields: poorer photometric data for F1 precluded secure detection of the single massive cluster at z = 0.35 that we find in SHELS. Taken together, the two fields include 16,055 redshifts for galaxies with R{sub 0}⩽20.2 and 20,754 redshifts for galaxies with R ≤ 20.6. These dense surveys in two well-separated fields provide a basis for future investigations of galaxy properties and large-scale structure.

79 ASTRONOMY AND ASTROPHYSICS↗