We investigate turbulence generated by cosmological structure formation by means of large eddy simulations using adaptive mesh refinement. In contrast to the widely used implicit large eddy simulations, which resolve a limited range of length scales and treat the effect of turbulent velocity fluctuations below the grid scale solely by numerical dissipation, we apply a subgrid-scale model for the numerically unresolved fraction of the turbulence energy. For simulations with adaptive mesh refinement, we utilize a new methodology that allows us to adjust the scale-dependent energy variables in such a way that the sum of resolved and unresolved energies is globally conserved. We test our approach in simulations of randomly forced turbulence, a gravitationally bound cloud in a wind, and the Santa Barbara cluster. To treat inhomogeneous turbulence, we introduce an adaptive Kalman filtering technique that separates turbulent velocity fluctuations on resolved length scales from the non-turbulent bulk flow. From the magnitude of the fluctuating component and the subgrid-scale turbulence energy, a total turbulent velocity dispersion of several 100 km/s is obtained for the Santa Barbara cluster, while the low-density gas outside the accretion shocks is nearly devoid of turbulence. The energy flux through the turbulent cascade and the dissipation rate predicted by the subgrid-scale model correspond to dynamical time scales around 5 Gyr, independent of numerical resolution.
Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.
In this work, we consider an interacting dark-fluid cosmological model in which energy exchange between dark matter and dark energy occurs through diffusion. After solving the background expansion history for a late-time universe, we attempt to constrain the cosmological parameters by comparing simulated values of the model against Supernovae Type 1A data. We consider four different cases and compare them against the ΛCDM model as the "true model". Our results show that the diffusive model in which dark energy flows to dark matter is the most likely alternative to ΛCDM model. This model is not only in line with Planck 2018 observational results but can also give a potential explanation to the so-called Hubble tension.
Based on the Marenostrum-MultiDark SImulation of galaxy Clusters (MUSIC-2) we develop semi-analytical models which provide multi-wavelength emission maps generated by dark matter (DM) annihilation processes in galaxy clusters and their sub-halos. We focus on radio and gamma-ray emission maps from neutralino DM annihilation processes testing two different neutralino masses, M χ , 35 GeV and 60 GeV along with two different models of magnetic fields. A comparison of the radio flux densities from our DM annihilation model with the observed diffuse radio emission from the Coma cluster shows that they are of the same order of magnitude. We determine the DM densities with a Smoothed Particle Hydrodynamics (SPH) kernel. This enables us to integrate the DM annihilation signal along any given line-of-sight through the volume of the cluster. In particular it allows us to investigate the contribution of sub-halos to the DM annihilation signal with very high resolution. Zooming in on a subset of high mass-to-light ratio DM sub-halos, i.e. DM sub-halos with very low baryon content, we demonstrate that such targets can generate prominent annihilation signals. The radial distribution of high mass-to-light ratio (M/L) DM sub-halos is more strongly peaked at ≈ R 200crit compared to the distribution of all sub-halos which may suggest that the search for DM annihilation signals from sub-halos in clusters is most promising at R 200crit . The radio flux densities from DM sub-halos are well within the sensitivity limit of Square Kilometer Array (SKA) with an integration time of 1000 hours and unlike clusters their gamma-ray spectrum is seen to be dominated by pion decay over a wide range of gamma-ray energies. Our model makes clear predictions for future radio and gamma-ray observations of the DM annihilation signal in clusters and their sub-halos.
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