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Huiyuan Wang - One of the best experts on this subject based on the ideXlab platform.

  • elucid exploring the local universe with the reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    The Astrophysical Journal, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input nonLinear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h(-1) Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k similar to 0.85 h Mpc(-1) at high z and to k similar to 3.4 h Mpc(-1) at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

  • elucid exploring the local universe with reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    arXiv: Cosmology and Nongalactic Astrophysics, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input non-Linear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle Mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. The constrained simulations so obtained accurately reproduce both the amplitudes and phases of the input simulations at various $z$. Using a PM model with a grid cell size of 0.75 Mpc/h and 40 time-steps in the HMC can recover more than half of the phase information down to a scale k~0.85 h/Mpc at high z and to k~3.4 h/Mpc at z=0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

Weipeng Lin - One of the best experts on this subject based on the ideXlab platform.

  • elucid exploring the local universe with the reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    The Astrophysical Journal, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input nonLinear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h(-1) Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k similar to 0.85 h Mpc(-1) at high z and to k similar to 3.4 h Mpc(-1) at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

  • elucid exploring the local universe with reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    arXiv: Cosmology and Nongalactic Astrophysics, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input non-Linear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle Mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. The constrained simulations so obtained accurately reproduce both the amplitudes and phases of the input simulations at various $z$. Using a PM model with a grid cell size of 0.75 Mpc/h and 40 time-steps in the HMC can recover more than half of the phase information down to a scale k~0.85 h/Mpc at high z and to k~3.4 h/Mpc at z=0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

Xiaohu Yang - One of the best experts on this subject based on the ideXlab platform.

  • elucid exploring the local universe with the reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    The Astrophysical Journal, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input nonLinear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h(-1) Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k similar to 0.85 h Mpc(-1) at high z and to k similar to 3.4 h Mpc(-1) at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

  • elucid exploring the local universe with reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    arXiv: Cosmology and Nongalactic Astrophysics, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input non-Linear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle Mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. The constrained simulations so obtained accurately reproduce both the amplitudes and phases of the input simulations at various $z$. Using a PM model with a grid cell size of 0.75 Mpc/h and 40 time-steps in the HMC can recover more than half of the phase information down to a scale k~0.85 h/Mpc at high z and to k~3.4 h/Mpc at z=0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

Y P Jing - One of the best experts on this subject based on the ideXlab platform.

  • elucid exploring the local universe with the reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    The Astrophysical Journal, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input nonLinear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h(-1) Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k similar to 0.85 h Mpc(-1) at high z and to k similar to 3.4 h Mpc(-1) at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

  • elucid exploring the local universe with reconstructed initial Density field i hamiltonian markov chain monte carlo method with particle mesh dynamics
    arXiv: Cosmology and Nongalactic Astrophysics, 2014
    Co-Authors: Huiyuan Wang, Xiaohu Yang, Y P Jing, Weipeng Lin
    Abstract:

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial Linear Density field from an input non-Linear Density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle Mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed Linear Density fields are then evolved to the present day with N-body simulations. The constrained simulations so obtained accurately reproduce both the amplitudes and phases of the input simulations at various $z$. Using a PM model with a grid cell size of 0.75 Mpc/h and 40 time-steps in the HMC can recover more than half of the phase information down to a scale k~0.85 h/Mpc at high z and to k~3.4 h/Mpc at z=0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed Linear Density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

Kothari V K - One of the best experts on this subject based on the ideXlab platform.

  • Moisture management and wicking properties of polyester- cotton plated knits
    Indian Journal of Fibre & Textile Research (IJFTR), 2017
    Co-Authors: Jhanji Yamini, Gupta Deepti, Kothari V K
    Abstract:

    Effect of yarn Linear Density on moisture management and wicking properties of polyester-cotton plated knit structures has been studied. Linear Density of yarns used in inner and outer layer as well as the difference in the yarn Linear Density for the two layers have been found to affect the liquid transfer from inner to outer layer, liquid spreading in the outer layer and hence the drying ability of the designed fabrics. Wetting time increases, while decrease in absorption rate and spreading speed is observed with the increase in inner and outer layer yarn Linear Density. The fabrics are graded and classified based on the obtained moisture management indices. Trans planar wicking is found higher for fabrics with greater difference in Linear Density between inner and outer layers as a result of selection of finer yarns in inner layer

  • Effect of Linear Density of feed yarn filaments and air-jet texturing process variables on compressional properties of fabrics
    NISCAIR-CSIR India, 2017
    Co-Authors: Aldua R K, Rengasamy R S, Kothari V K
    Abstract:

    9-16Effect of filament fineness and process parameters employed in the production of air-jet textured yarns has been studied on the compression and recovery of union fabrics made from air-jet textured yarns as weft and twisted filament yarns as warp. Filament Linear Density and process parameters such as overfeed, air pressure and texturing speed affect the textured yarn structure and hence fabric properties. The individual effect of filament fineness and process variables in the production of air-jet textured yarn has been studied in terms of potential contribution and normalized regression coefficient on fabric low load compression behavior. Fabric low load compression-recovery behavior has been analyzed in terms of compression parameter, recovery parameter and resiliency. Analysis shows that most dominating factor to explain the low load compression properties of air-jet textured yarn fabric is overfeed percentage, while Linear Density per filament is most dominating factor affecting fabric resiliency

  • Effect of Linear Density of feed yarn filaments and air-jet texturing process variables on compressional properties of woven fabrics
    NISCAIR-CSIR India, 2016
    Co-Authors: Aldua R K, Rengasamy R S, Kothari V K
    Abstract:

    47-54The influence of yarn feed and process parameters used in the production of air-jet textured yarn on compression and recovery behavior of air-jet textured yarn fabric has been studied. Yarn Linear Density per filament and air-jet texturing process parameters, such as overfeed, air pressure and texturing speed are the key factors which influence yarn structure and hence fabric properties. The individual effect of feed yarn properties and air-jet process variables in the production of air-jet textured yarn has been studied in term of potential contribution and normalized regression coefficient on fabric low load compression behavior. Fabric low load compression-recovery behavior has been analyzed by defining compression parameter, recovery parameter and resiliency. Analysis shows that most dominating factor to explain the air-jet textured yarn fabric low-load compression properties is overfeed percentage, while Linear Density per filament is most dominating factor affecting fabric resiliency