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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

Security in the CernVM File System and the Frontier Distributed Database Caching System

Both the CernVM File System (CVMFS) and the Frontier Distributed Database Caching System (Frontier) distribute centrally updated data worldwide for LHC experiments using http proxy caches. Neither system provides privacy or access control on reading the data, but both control access to updates of the data and can guarantee the authenticity and integrity of the data transferred to clients over the internet. CVMFS has since its early days required digital signatures and secure hashes on all distributed data, and recently Frontier has added X.509-based authenticity and integrity checking. In this paper we detail and compare the security models of CVMFS and Frontier.

Dykstra, D.↗
DOE OSTI2022

The CMS Magnetic Field Measuring and Monitoring Systems

This review article describes the performance of the magnetic field measuring and monitoring systems for the Compact Muon Solenoid (CMS) detector. To cross-check the magnetic flux distribution obtained with the CMS magnet model, four systems for measuring the magnetic flux density in the detector volume were used. The magnetic induction inside the 6 m diameter superconducting solenoid was measured and is currently monitored by four nuclear magnetic resonance (NMR) probes installed using special tubes outside the barrel hadron calorimeter. Two more NRM probes were installed at the faces of the tracking system. The field inside the superconducting solenoid was precisely measured in 2006 in a cylindrical volume of 3.448 m in diameter and 7 m in length using 10 three-dimensional (3D) B-sensors based on the Hall effect. These B-sensors were installed on each of the two propeller arms of an automated field-mapping machine. In addition to these measurement systems, a system for monitoring the magnetic field during the CMS detector operation has been developed. Inside the solenoid in the horizontal plane, 4 3D B-sensors were installed at the faces of the tracking detector. Twelve 3D B-sensors were installed on the surfaces of the flux-return yoke nose disks. Seventy 3D B-sensors were installed in the air gaps of the CMS magnet yoke. A specially developed flux loop technique was used for the most complex measurements of the magnetic flux density inside the steel blocks of the CMS magnet yoke. The flux loops are installed in 22 sections of the flux-return yoke blocks. The areas enclosed by these coils varied from 0.3 to 1.59 m$^{2}$ in the blocks of the barrel wheels and from 0.5 to 1.12 m$^{2}$ in the blocks of the yoke endcap disks. The development of these systems and the results of the magnetic flux density measurements across the CMS magnet are presented and discussed in this review article.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Progress towards quantum simulating the classical O ( 2 ) mode

We connect explicitly the classical $O(2)$ model in 1+1 dimensions, a model sharing important features with $U(1)$ lattice gauge theory, to physical models potentially implementable on optical lattices and evolving at physical time. Using the tensor renormalization group formulation, we take the time continuum limit and check that finite dimensional projections used in recent proposals for quantum simulators provide controllable approximations of the original model. We propose two-species Bose-Hubbard models corresponding to these finite dimensional projections at strong coupling and discuss their possible implementations on optical lattices using a $^{87}$Rb and $^{41}$K Bose-Bose mixture.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2019

Active Measurement of Plasma Parameters in Over-Dense MFE Reactor Plasma Using High Frequency Beat-Wave Resonantly Generated Thermal Bernstein Waves

UC Davis was funded to explore the combination of beat-wave excitation and laser scattering to make completely non-invasive determination of local plasma conditions. This involves the use of two high frequency mm-wave sources of differing frequencies (the pump waves) to excite plasma waves at the difference of the frequencies, which is known as the beat frequency. The difference frequency or the frequency of the beat wave is chosen so that we produce plasma waves whose fluctuating electron density is a sensitive function of plasma parameters at the location where the source waves overlap. This fluctuation can be detected from outside the plasma, by measuring the deflection of a probe beam directed at the overlap region between the two pump waves (i.e., collective scattering). This work is an extension of mm-wave frequencies of a 10 GHz beat-wave experiment conducted on the DDT toroidal device (see Fig. 1) in a magnetized plasma where the excited Langmuir wave is replaced by the magnetized upper hybrid wave. Here, two high power (> 1 kW) ~94 GHz pump sources will be employed to match the upper hybrid resonance. The primary envisioned experiment is to drive the electron Bernstein wave (EBW) in the interior of a magnetized plasma. The EBW is a hot-plasma electrostatic wave, which cannot propagate in vacuum. Roots of the EBW dispersion relation occur at frequencies slightly higher than harmonics of the electron cyclotron frequency. Use of the EBW has particular advantages for measuring plasma magnetic field in plasmas with relatively high density and low magnetic field, as is the case in spherical tokamaks. This is the “over dense” condition of future efficient high beta magnetic confinement fusion reactors such as the spherical tokamak (STs). The overarching goal of the proposed project is to demonstrate beat wave EBW excitation in low density and temperature parameters that are directly scalable to over-dense spherical tokamaks such as NSTX or MAST, where measurement of magnetic field by electron cyclotron emission (ECE) is experimentally complex, and requires models of plasma profiles in order to interpret results. In such experiments, availability of point measurements of magnetic field strength provided by a beat-wave diagnostic would serve as a powerful independent check on the ECE results, serving to validate the assumed profiles and wave-propagation models used. The first step was to assemble/modify the target chamber in which the beat-wave plasma experiments will take place. Here, the target chamber of the CTIX compact-toroid (spheromak) injector was chosen. At the time that the proposal was submitted, the aim was to conduct the experiments in the CTIX experimental facilities located at the Livermore campus of UC Davis using microwave/mm-wave equipment brought from the main campus., However, by the time DoE funds for this project were awarded, the CTIX experimental facilities located at the Livermore campus of UC Davis were in the process of being shut down due to the closure of the Livermore campus by Lawrence Livermore National Laboratory (LLNL). Consequently, the equipment needed for this project was crated up and shipped from Livermore to Davis as part of the laboratory shutdown after space on campus was secured and College of Engineering approval secured. Once it arrived in Davis on December 20, 2018, work on the project began in earnest with the initial efforts directed toward developing a new plasma target source. Because of the shutdown of the CTIX facility, the scope of the project thus had to be reduced due to the abbreviated amount of time allocated to the project once the shutdown/move had been completed. Chamber modifications have been completed, including the fabrication and installation of a 38 GHz interferometer, as described in Sec. 3. The thermionic plasma source has been tested and characterized. The magnetic field required for the EBW experiments will be generated by permanent magnets mounted on plates positioned above and below the chamber. Magnetic simulations have been performed, and show that a 3-ring magnet arrangement on each plate, with a plate separation of 50 cm, is required to achieve the ~100 G uniform magnetic field in the interaction region that we feel is necessary to ensure good EBW coupling. We proved unable to successfully revive either of Prof. Luhmann’s two high power (≥20 kW) 94 GHz millimeter-wave sources after their extended time in storage and decided to postpone further efforts and concentrate on slightly lower power sources. Consequently, we were successful in reviving a ~2 kW extended klystron amplifier (EIK), which together with a ~2 kW extended interaction oscillator (EIO) will provide the two pump sources required for the project. Safe operation of the EIO requires extensive modifications to one of Prof. Luhmann’s pulse modulators - these modulator modifications are ongoing, which has unfortunately delayed the planned beat-wave experiments. Details of this work are provided in Sec. 3. Even though project funding has now ended, we still plan to proceed to complete the EIO pulse modulator modifications, install the two ~94 GHz pump wave sources on the target chamber, and conduct unmagnetized tests of the beat-wave concept which involve coupling to unmagnetized electron plasma waves rather than magnetized EBWs. Should these experiments prove successful, we plan to submit a follow-up proposal for additional funding which would allow us to undertake the magnetized EBW experiments.

42 ENGINEERING↗
DOE OSTI2018

Robust Bayesian Calibration of a k-ε Model for Compressible Jet-in-Crossflow Simulations

Compressible jet-in-crossflow interactions are difficult to simulate accurately using Reynolds-averaged Navier–Stokes (RANS) models. This could be due to simplifications inherent in RANS or the use of inappropriate RANS constants estimated by fitting to experiments of simple or canonical flows. Our previous work on Bayesian calibration of a k-ε model to experimental data had led to a weak hypothesis that inaccurate simulations could be due to inappropriate constants more than model-form inadequacies of RANS. In this work, Bayesian calibration of k-ε constants to a set of experiments that span a range of Mach numbers and jet strengths has been performed. The variation of the calibrated constants has been checked to assess the degree to which parametric estimates compensate for RANS’s model-form errors. An analytical model of jet-in-crossflow interactions has also been developed, and estimates of k-ε constants that are free of any conflation of parametric and RANS’s model-form uncertainties have been obtained. It has been found that the analytical k-ε constants provide mean-flow predictions that are similar to those provided by the calibrated constants. Further, both of them provide predictions that are far closer to experimental measurements than those computed using “nominal” values of these constants simply obtained from the literature. It can be concluded that the lack of predictive skill of RANS jet-in-crossflow simulations is mostly due to parametric inadequacies, and our analytical estimates may provide a simple way of obtaining predictive compressible jet-in-crossflow simulations.

42 ENGINEERING↗
DOE OSTI2019

Difference distributions applicable to certain health physic's measurements

This paper discusses calculational methods for the determination of the difference distributions associated with certain health physics measurements. These measurements include the check source response counts relative to an initial reference count, the Albatross (i.e., HPI model 2080B) neutron tube counts relative to the gamma tube counts, and those that involve the use of the automatic background subtraction feature within portable health physics instrumentation. This paper therefore provides a technical basis for the necessary source strength of a check source in order to meet daily limits, the gamma field limitations of the HPI2080 Albatross, as well as the consequences of automatic background subtraction. Examples are provided that illustrate the methods for a few specific measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnel data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.

42 ENGINEERING↗
DOE OSTI2019

Assembly and Tests of Mechanical Models of the 15-T Nb 3 Sn Dipole Demonstrator

Within the US Magnet Development Program, Fermilab is developing a 15-T Nb 3 Sn dipole demonstrator. Prior to the construction of the real magnet model, short sections and the whole structure were instrumented with strain gauges and assembled to confirm the results of structural analysis, check tooling and to gain experience with the assembly of the real magnet components. We summarize the lessons learned from these mechanical models and compares the measured data with the finite element analysis.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Simulation process and data flow for a large system dynamics model

This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis process at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.

09 BIOMASS FUELS↗
DOE OSTI2016

Advances in Westinghouse fuel rod design technology (PAD5)

The Westinghouse Performance Analysis and Design (PAD) code for light-water reactors has been recently updated to version PAD5 and submitted to NRC for review. PAD5 incorporates extensive updates in fuel performance and material property models, including fuel thermal conductivity, pellet fragment relocation, fission gas release, fission gas swelling, and cladding creep and growth. Advances in thermal models are mainly in pellet fragment relocation, radial power distribution within the pellet, and the changes in fuel thermal conductivity due to changes in temperature, burnup and fuel porosity. The PAD5 thermal fission gas release model is a phenomenological model based on the irradiation dependence of the gas release from mechanistic processes for the diffusion of the fission gas generated in the fuel grains to the grain boundaries, formation of fission gas bubbles on the grain boundaries and release of the fission gas when these grain boundary bubbles inter-link. The PAD5 cladding creep model is predominantly a combination of the following mechanisms: thermal creep, irradiation induced creep, and irradiation enhanced creep. The fuel performance database used for the development, calibration and testing of the PAD5 code covers a broad range of both commercial irradiation data and test rod data and requires continuously additional data focusing on the latest development of the PAD5 code. Test programs are also included in the database. These data are checked and stored in a hierarchical database structure that supports the automation of model development and calibration. New data, once incorporated into the database, can be quickly evaluated with respect to key fuel and cladding performance models to confirm expected trends or to evaluate potential model improvements. This automated calibration system has been integrated with state-of-the-art optimization packages in DAKOTA Analysis Kit to provide for advanced statistical assessments and model coefficient optimization. Calibration results are automatically tabulated and plotted to provide easy assessment of trends or new behaviors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Verification of iterative matrix solutions for multipoint kinetics equations

Multipoint kinetic equation systems have been solved numerically using matrix algebra software and 1st to 4th order implicit schemes based on single-step matrix propagators. These matrix schemes have been validated successfully on demanding point kinetics benchmarks with various prescribed reactivity insertions. Verification tests have also been performed on a simple 3-region fast reactor core model with asymmetric step or ramp reactivity insertions and cross-checked with the multipoint kinetics SACRE code developed at INL and based on existing and validated stiff ODE solver packages. Accurate results can be obtained at a limited time expense. The reason for intriguing results obtained in some transients has been elucidated and linked to the multipoint matrix coefficient interpolation method during the transient.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Erroneous predictions of auxotrophies by CarveMe

We report in their paper, D. Machado et al. claim to identify ‘cooperative’ communities with many amino acid auxotrophies1. A related paper, with many of the same authors, claims that 26% of bacteria from free-living communities are amino acid auxotrophs2. The putative amino acid auxotrophies were derived from genome sequences via the metabolic models of CarveMe3 but were not validated in any way. I checked the predictions of CarveMe for prototrophic bacteria and found a high rate of spurious auxotrophies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

NUCLEAR INFRARED SPECTRAL ENERGY DISTRIBUTION OF TYPE II ACTIVE GALACTIC NUCLEI

We present near- and mid-IR observations of a sample of Seyfert II galaxies drawn from the 12 {mu}m Galaxy sample. The sample was observed in the J, H, K, L, M and N bands. Galaxy surface brightness profiles are modeled using nuclear, bulge, bar (when necessary), and disk components. To check the reliability of our findings, the procedure was tested using Spitzer observations of M 31. Nuclear spectral energy distributions (SEDs) are determined for 34 objects, and optical spectra are presented for 38, including analysis of their stellar populations using the STARLIGHT spectral synthesis code. Emission line diagnostic diagrams are used to discriminate between genuine active galactic nuclei (AGNs) and H II nuclei. Combining our observations with those found in the literature, we have a total of 40 SEDs. It is found that about 40% of the SEDs are characterized by an upturn in the near-IR, which we have quantified as a NIR slope {alpha} < 1 for an SED characterized as {lambda}f {sub {lambda}}{proportional_to}{lambda}{sup {alpha}}. The three objects with an H II nucleus and two Seyfert nuclei with strong contamination from a circumnuclear also show an upturn. For genuine AGNs, this component could be explained as emission from the accretion disk, a jet, or from a very hot dust component leaking from the central region through a clumpy obscuring structure. The presence of a very compact nuclear starburst as the origin for this NIR excess emission is not favored by our spectroscopic data for these objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A UNIVERSAL POWER-LAW PROFILE OF PSEUDO-PHASE-SPACE DENSITY-LIKE QUANTITIES IN ELLIPTICAL GALAXIES

We study profiles of mass density, velocity dispersion (VD), and a combination of both using ∼2000 nearly spherical and rotation-free Sloan Digital Sky Survey galaxies. For observational stellar mass density ρ{sub *}(r), we consider a range of dark matter (DM) distribution ρ{sub DM}(r) and VD anisotropy β(r) to investigate radial stellar VD σ{sub *r}(r) using the spherical Jeans equation. While mass and VD profiles vary appreciably depending on DM distribution and anisotropy, the pseudo-phase-space density-like combination ρ(r)/σ{sub ⋆r}{sup 3}(r) with total density ρ(r) = ρ{sub *}(r) + ρ{sub DM}(r) is nearly universal. In the optical region, the negative logarithmic slope has a mean value of (χ) ≈ 1.86-1.90 with a galaxy-to-galaxy rms scatter of ≈0.04-0.06, which is a few times smaller than that of ρ(r) profiles. The scatter of χ can be increased by invoking wildly varying anisotropies that are, however, less likely because they would produce too large a scatter of line of sight VD profiles. As an independent check of this universality, we analyze stellar orbit-based dynamical models of 15 early-type galaxies (ETGs) of the Coma cluster provided by J. Thomas. Coma ETGs, with σ{sub *r}(r) replaced by the rms velocity of stars v {sub *rms}(r) including net rotation, exhibit a similar universality with a slope of χ = 1.93 ± 0.06. Remarkably, the inferred values of χ for ETGs match well the slope ≈1.9 predicted by N-body simulations of DM halos. We argue that the inferred universal nature of ρ(r)/σ{sub ⋆r}{sup 3}(r) cannot be fully explained by equilibrium alone, implying that some astrophysical factors conspire and/or it reflects a fundamental principle in collisionless formation processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing

We present cosmological results from a combined analysis of galaxy clustering and weak gravitational lensing, using 1321 deg2 of griz imaging data from the first year of the Dark Energy Survey (DES Y1). We combine three two-point functions: (i) the cosmic shear correlation function of 26 million source galaxies in four redshift bins, (ii) the galaxy angular autocorrelation function of 650,000 luminous red galaxies in five redshift bins, and (iii) the galaxy-shear cross-correlation of luminous red galaxy positions and source galaxy shears. To demonstrate the robustness of these results, we use independent pairs of galaxy shape, photometric-redshift estimation and validation, and likelihood analysis pipelines. To prevent confirmation bias, the bulk of the analysis was carried out while “blind” to the true results; we describe an extensive suite of systematics checks performed and passed during this blinded phase. The data are modeled in flat ΛCDM and wCDM cosmologies, marginalizing over 20 nuisance parameters, varying 6 (for ΛCDM) or 7 (for wCDM) cosmological parameters including the neutrino mass density and including the 457×457 element analytic covariance matrix. We find consistent cosmological results from these three two-point functions and from their combination obtain S8≡σ8(Ωm/0.3)0.5=0.773-0.020+0.026 and Ωm=0.267-0.017+0.030 for ΛCDM; for wCDM, we find S8=0.782-0.024+0.036, Ωm=0.284-0.030+0.033, and w=-0.82-0.20+0.21 at 68% C.L. The precision of these DES Y1 constraints rivals that from the Planck cosmic microwave background measurements, allowing a comparison of structure in the very early and late Universe on equal terms. Although the DES Y1 best-fit values for S8 and Ωm are lower than the central values from Planck for both ΛCDM and wCDM, the Bayes factor indicates that the DES Y1 and Planck data sets are consistent with each other in the context of ΛCDM. Combining DES Y1 with Planck, baryonic acoustic oscillation measurements from SDSS, 6dF, and BOSS and type Ia supernovae from the Joint Lightcurve Analysis data set, we derive very tight constraints on cosmological parameters: S8=0.802±0.012 and Ωm=0.298±0.007 in ΛCDM and w=-1.00-0.04+0.05 in wCDM. Upcoming Dark Energy Survey analyses will provide more stringent tests of the ΛCDM model and extensions such as a time-varying equation of state of dark energy or modified gravity.

79 ASTRONOMY AND ASTROPHYSICS↗