Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model structural error”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Understanding reliability and some limitations of the images and spectra reconstructed from a multi-monochromatic x-ray imager

Temperature and density asymmetry diagnosis is critical to advance inertial confinement fusion (ICF) science. A multi-monochromatic x-ray imager (MMI) is an attractive diagnostic for this purpose. The MMI records the spectral signature from an ICF implosion core with time resolution, 2-D space resolution, and spectral resolution. While narrow-band images and 2-D space-resolved spectra from the MMI data constrain temperature and density spatial structure of the core, the accuracy of the images and spectra depends not only on the quality of the MMI data but also on the reliability of the post-processing tools. In this paper, we synthetically quantify the accuracy of images and spectra reconstructed from MMI data. Errors in the reconstructed images are less than a few percent when the space-resolution effect is applied to the modeled images. The errors in the reconstructed 2-D space-resolved spectra are also less than a few percent except those for the peripheral regions. Spectra reconstructed for the peripheral regions have slightly but systematically lower intensities by ~6% due to the instrumental spatial-resolution effects. However, this does not alter the relative line ratios and widths and thus does not affect the temperature and density diagnostics. We also investigate the impact of the pinhole size variation on the extracted images and spectra. A 10% pinhole size variation could introduce spatial bias to the images and spectra of ~10%. A correction algorithm is developed, and it successfully reduces the errors to a few percent. Finally, it is desirable to perform similar synthetic investigations to fully understand the reliability and limitations of each MMI application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

An operator splitting approach for the interaction between a fluid and a multilayered poroelastic structure

We develop a loosely coupled fluid‐structure interaction finite element solver based on the Lie operator splitting scheme. The scheme is applied to the interaction between an incompressible, viscous, Newtonian fluid, and a multilayered structure, which consists of a thin elastic layer and a thick poroelastic material. The thin layer is modeled using the linearly elastic Koiter membrane model, while the thick poroelastic layer is modeled as a Biot system. We prove a conditional stability of the scheme and derive error estimates. Theoretical results are supported with numerical examples. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 31: 1054–1100, 2015

Bukač, Martina↗
DOE OSTI2014

Stochastic solution of population balance equations for reactor networks

This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images. Highlights: •An algorithm is presented to solve reactor networks with a population balance model. •A stochastic method is used to solve the population balance equations. •The convergence and efficiency of the reported algorithms are evaluated. •The algorithm is applied to simulate silicon nanoparticle synthesis in a 1D reactor. •Particle structure is reported as a function of reactor length and time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Quantitative phase analysis of challenging samples using neutron powder diffraction. Sample #4 from the CPD QPA round robin revisited

Here, quantitative phase analysis (QPA) using neutron powder diffraction more often than not involves non-ambient studies where no sample preparation is possible. The larger samples and penetration of neutrons versus X-rays makes neutron diffraction less susceptible to inhomogeneity and large grain sizes, but most well-characterized QPA standard samples do not have these characteristics. Sample #4 from the International Union of Crystallography Commission on Powder Diffraction QPA round robin was one such sample. Data were collected using the POWGEN time-of-flight (TOF) neutron powder diffractometer and analysed together with historical data from the C2 diffractometer at Chalk River. The presence of magnetic reflections from Fe 3 O 4 (magnetite) in the sample was an additional consideration, and given the frequency at which iron-containing and other magnetic compounds are present during in-operando studies their possible impact on the accuracy of QPA is of interest. Additionally, scattering from thermal diffuse scattering in the high- Q region (<0.6 Å) accessible with TOF data could impact QPA results during least-squares because of the extreme peak overlaps present in this region. Refinement of POWGEN data was largely insensitive to the modification of longer d -spacing reflections by magnetic contributions, but the constant-wavelength data were adversely impacted if the magnetic structure was not included. A robust refinement weighting was found to be effective in reducing quantification errors using the constant-wavelength neutron data both where intensities from magnetic reflections were ignored and included. Results from the TOF data were very sensitive to inadequate modelling of the high- Q (low d -spacing) background using simple polynomials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Quantum Chemical Prediction of p K a Values of Cationic Ion-Exchange Groups in Polymer Electrolyte Membranes

The durability of anion-exchange membranes (AEMs) in alkaline fuel cells is largely determined by the stability of anion-exchange groups. Despite continuous research efforts, the commonly employed cations still have limited stability against hydroxide that can act as a strong base and nucleophile. This work is concerned with base-catalyzed degradation of organic cations initiated by proton abstraction to form reactive ylides or carbenes. We report on the performance of 24 methods combining density functional theory and electronic structure methods with implicit solvation calculations for predicting pK a values of organic cations in water and DMSO. The most accurate computational protocols are obtained using a combination of M06-2X/6-311++G** with the SMD solvation model for water (the mean absolute error of 0.4 pK a units) and B3LYP/aug-cc-pVTZ with the IEFPCM solvation model for DMSO (the mean absolute error of 1.4 pK a units). The aqueous pK a calculation protocol is cross-validated against the experimental C–H acidity constants outside the conventional range of 0–14 pK a values. In conclusion, this study rationalizes alkaline degradation of imidazolium cations with C2-alkyl substituents and provides a theoretical scale of C–H acidity for potential anion-exchange groups in AEMs.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Further, our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Weighing 'El Gordo' with a precision scale: Hubble space telescope weak-lensing analysis of the merging galaxy cluster ACT-CL J0102–4915 at z = 0.87

We present a Hubble Space Telescope weak-lensing study of the merging galaxy cluster 'El Gordo' (ACT-CL J0102–4915) at z = 0.87 discovered by the Atacama Cosmology Telescope (ACT) collaboration as the strongest Sunyaev-Zel'dovich decrement in its ∼1000 deg{sup 2} survey. Our weak-lensing analysis confirms that ACT-CL J0102–4915 is indeed an extreme system consisting of two massive (≳ 10{sup 15} M {sub ☉} each) subclusters with a projected separation of ∼0.7 h{sub 70}{sup −1} Mpc. This binary mass structure revealed by our lensing study is consistent with the cluster galaxy distribution and the dynamical study carried out with 89 spectroscopic members. We estimate the mass of ACT-CL J0102–4915 by simultaneously fitting two axisymmetric Navarro-Frenk-White (NFW) profiles allowing their centers to vary. We use only a single parameter for the NFW mass profile by enforcing the mass-concentration relation from numerical simulations. Our Markov-Chain-Monte-Carlo analysis shows that the masses of the northwestern (NW) and the southeastern (SE) components are M{sub 200c}=(1.38±0.22)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙} and (0.78±0.20)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙}, respectively, where the quoted errors include only 1σ statistical uncertainties determined by the finite number of source galaxies. These mass estimates are subject to additional uncertainties (20%-30%) due to the possible presence of triaxiality, correlated/uncorrelated large scale structure, and departure of the cluster profile from the NFW model. The lensing-based velocity dispersions are 1133{sub −61}{sup +58} km s{sup −1} and 1064{sub −66}{sup +62} km s{sup −1} for the NW and SE components, respectively, which are consistent with their spectroscopic measurements (1290 ± 134 km s{sup –1} and 1089 ± 200 km s{sup –1}, respectively). The centroids of both components are tightly constrained (∼4'') and close to the optical luminosity centers. The X-ray and mass peaks are spatially offset by ∼8'' (∼62 h{sub 70}{sup −1} kpc), which is significant at the ∼2σ confidence level. The mass peak, however, does not lead the gas peak in the direction expected if we are viewing the cluster soon after first core passage during a high speed merger. Under the assumption that the merger is happening in the plane of the sky, extrapolation of the two NFW halos to a radius r{sub 200a}=2.4 h{sub 70}{sup −1} Mpc yields a combined mass of M{sub 200a}=(3.13±0.56)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙}. This extrapolated total mass is consistent with our two-component-based dynamical analysis and previous X-ray measurements, projecting ACT-CL J0102–4915 to be the most massive cluster at z > 0.6 known to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2017

Heterogeneous Neutron Leakage Model for PWR Pin-by-pin Calculation

In the last few decades, the nodal diffusion calculation accompanied with the assembly homogenization has been widely employed. The single assembly calculation with the reflective boundary condition (BC) would be carried out to determine the assembly-homogenized parameters. Because of the reflective BC, no axial and/or radial leakage rates have been taken into accounted for the lattice calculation. To represent the global influence of the reactor core on an assembly calculation, one mostly frequently uses a procedure called leakage model to approximately take into account the absorption due to the macroscopic structure of the physical flux in a heterogeneous situation. For the assembly homogenization, this absorption is introduced as the product of the leakage coefficient and the buckling obtained for an equivalent homogeneous medium using the well-known homogeneous B1 theory. Clearly, this is an imperfect representation of leakages in a heterogeneous medium. However, the errors introduced by the foregoing procedure are small for the whole? -assembly homogenization in a PWR. When it comes to the pin-by-pin calculation, it is not the case. The leakage rate? will be affected by the neutron streaming effect caused by the heterogeneity of the assembly. Especially when poison pins or empty channels are present, the streaming effect becomes important. The errors introduced appear particularly visible. The heterogeneous leakage model? which assumes that the neutron flux is variational and the leakage rate is not uniform over the lattice pitch is a more appropriate choice. When the heterogeneous leakage model is applied in the lattice calculation for the pin-cell homogenization, the pin-homogenized problem would be changed compared with the original assembly problem without any homogenization technique. So, the Cooperation of the heterogeneous leakage model with the pin-cell homogenization theory is unsettled. In this paper, both homogenous and heterogeneous leakage models are evaluated in pin-by-pin calculation. Their implements used in pin-cell homogenization of the lattice calculation are studied. The diffusion coefficient obtained from transport cross-section and the space-dependent leakage coefficient together with the two leakage models are analyzed for the practical reactor-core applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Predicting Device Parameters for Dye-Sensitized Solar Cells from Electronic Structure Calculations to Reproduce Experiment.

Given that improvements to the power-conversion efficiency (PCE) of dye-sensitized solar cells (DSSCs) have slowed in recent years, a means to accurately predict the device parameters yielded by trial dyes in silico, without having to synthesize them, would be extremely valuable to speed up the design process. Currently, the best-performing methods of calculating device parameters rely on a set of experimentally determined kinetic coefficients. In practice, it is very difficult to measure these kinetic parameters accurately, limiting the overall accuracy of such predictive methods. This work proposes a model to obtain key parameters such as J(SC), V-OC, and PCE using only the results from density functional theory (DFT) and time-dependent DFT calculations, noting that rates of electron-transfer steps are ultimately linked to the electronic structure of the dye center dot center dot center dot TiO2 working electrode. Six organic DSSC dyes from dissimilar chemical classes (L0, L1, L2, WS-2, WS-92, and C281) were chosen to demonstrate the power of this approach. Their a priori known experimentally determined device performance metrics served to validate our predictions. The greatest absolute error in our predicted PCE values was 0.36% relative to the experiment, while the greatest fractional error was 0.042. This indicates that the proposed model offers a dramatic improvement on previous predictive methods for DSSC device parameters, both in accuracy and in consistency. Moreover, such a predictive model has great potential to be applied to other photovoltaic applications, further enabling the design of novel, highly efficient photoactive materials.

density functional theory↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy

The spectral invariants theory ( p -theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the p-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. Further, the coefficient of determination (R²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3% for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4% for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3% to 42.6% for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.

3D heterogeneous structure↗
DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability

Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

“Best” Iterative Coupled-Cluster Triples Model? More Evidence for 3CC

To follow up on the unexpectedly good performance of several coupled-cluster models with approximate inclusion of 3-body clusters we performed a more complete assessment of the 3CC method for accurate computational thermochemistry in the standard HEAT framework. New spin-integrated implementation of the 3CC method applicable to closed- and open-shell systems utilizes a new automated toolchain for derivation, optimization, and evaluation of operator algebra in many-body electronic structure. We found that with a double-ζ basis set the 3CC correlation energies and their atomization energy contributions are almost always more accurate (with respect to the CCSDTQ reference) than the CCSDT model as well as the standard CCSD(T) model. The mean absolute errors in cc-pVDZ {3CC, CCSDT, and CCSD(T)} electronic (per valence electron) and atomization energies relative to the CCSDTQ reference for the HEAT data set, were {24, 70, 122} μE h /e and {0.46, 2.00, 2.58} kJ/mol, respectively. The mean absolute errors in the complete-basis-set limit {3CC, CCSDT, and CCSD(T)} atomization energies relative to the HEAT model reference, were {0.52, 2.00, and 1.07} kJ/mol, The significant and systematic reduction of the error by the 3CC method and its lower cost than CCSDT suggests it as a viable candidate for post- CCSD(T) thermochemistry applications, as well as the preferred alternative to CCSDT in general.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

A LIKELY CLOSE-IN LOW-MASS STELLAR COMPANION TO THE TRANSITIONAL DISK STAR HD 142527

With the uniquely high contrast within 0.''1 ({Delta}mag(L') = 5-6.5 mag) available using Sparse Aperture Masking with NACO at Very Large Telescope, we detected asymmetry in the flux from the Herbig Fe star HD 142527 with a barycenter emission situated at a projected separation of 88 {+-} 5 mas (12.8 {+-} 1.5 AU at 145 pc) and flux ratios in H, K, and L' of 0.016 {+-} 0.007, 0.012 {+-} 0.008, and 0.0086 {+-} 0.0011, respectively (3{sigma} errors), relative to the primary star and disk. After extensive closure-phase modeling, we interpret this detection as a close-in, low-mass stellar companion with an estimated mass of {approx}0.1-0.4 M{sub Sun }. HD 142527 has a complex disk structure, with an inner gap imaged in both the near and mid-IR as well as a spiral feature in the outer disk in the near-IR. This newly detected low-mass stellar companion may provide a critical explanation of the observed disk structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Filtered Backprojection in Compton Imaging using a Spherical Harmonic Wiener Filter with Pixelated CdZnTe

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. Here, this work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging-spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228Th sources placed 30° apart. The resulting filtered image of the two sources have an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach respectively; an improvement from the 29.0° FWHM image when using simple backprojection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Monte Carlo Control Loops for Cosmic Shear Cosmology with DES Year 1

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. The Monte Carlo Control Loops (MCCL) is a forward modelling method designed to tackle this problem. It relies on the Ultra Fast Image Generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum $C_{\ell}$ and the redshift distribution $n(z)$ of the background galaxy sample. The method includes maps of the systematic sources, Point Spread Function (PSF), an Approximate Bayesian Computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and the fast estimation of the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a non-tomographic setting, we derive a set of $C_\ell$ and $n(z)$ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of $\Omega_m$, $\sigma_8$, and intrinsic alignment amplitude $A_{\rm{IA}}$, defined as $S_8D_{\rm{IA}} = \sigma_8(\Omega_m/0.3)^{0.5}D_{\rm{IA}}$, where $D_{\rm{IA}}=1-0.11(A_{\rm{IA}}-1)$. We find $S_8D_{\rm{IA}}=0.895^{+0.054}_{-0.039}$, where systematics are at the level of roughly 60\% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

CLASH: PRECISE NEW CONSTRAINTS ON THE MASS PROFILE OF THE GALAXY CLUSTER A2261

We precisely constrain the inner mass profile of A2261 (z = 0.225) for the first time and determine that this cluster is not 'overconcentrated' as found previously, implying a formation time in agreement with {Lambda}CDM expectations. These results are based on multiple strong-lensing analyses of new 16-band Hubble Space Telescope imaging obtained as part of the Cluster Lensing and Supernova survey with Hubble. Combining this with revised weak-lensing analyses of Subaru wide-field imaging with five-band Subaru + KPNO photometry, we place tight new constraints on the halo virial mass M{sub vir} = (2.2 {+-} 0.2) Multiplication-Sign 10{sup 15} M{sub Sun} h {sup -1}{sub 70} (within r{sub vir} Almost-Equal-To 3 Mpc h {sup -1}{sub 70}) and concentration c{sub vir} = 6.2 {+-} 0.3 when assuming a spherical halo. This agrees broadly with average c(M, z) predictions from recent {Lambda}CDM simulations, which span 5 {approx}< (c) {approx}< 8. Our most significant systematic uncertainty is halo elongation along the line of sight (LOS). To estimate this, we also derive a mass profile based on archival Chandra X-ray observations and find it to be {approx}35% lower than our lensing-derived profile at r{sub 2500} {approx} 600 kpc. Agreement can be achieved by a halo elongated with a {approx}2:1 axis ratio along our LOS. For this elongated halo model, we find M{sub vir} = (1.7 {+-} 0.2) Multiplication-Sign 10{sup 15} M{sub Sun} h {sup -1}{sub 70} and c{sub vir} = 4.6 {+-} 0.2, placing rough lower limits on these values. The need for halo elongation can be partially obviated by non-thermal pressure support and, perhaps entirely, by systematic errors in the X-ray mass measurements. We estimate the effect of background structures based on MMT/Hectospec spectroscopic redshifts and find that these tend to lower M{sub vir} further by {approx}7% and increase c{sub vir} by {approx}5%.

79 ASTRONOMY AND ASTROPHYSICS↗