The MultiDark Database: Release of the Bolshoi and MultiDark Cosmological Simulations

We present the online MultiDark Database -- a Virtual Observatory-oriented, relational database for hosting various cosmological simulations. The data is accessible via an SQL (Structured Query Language) query interface, which also allows users to directly pose scientific questions, as shown in a number of examples in this paper. Further examples for the usage of the database are given in its extensive online documentation (www.multidark.org). The database is based on the same technology as the Millennium Database, a fact that will greatly facilitate the usage of both suites of cosmological simulations. The first release of the MultiDark Database hosts two 8.6 billion particle cosmological N-body simulations: the Bolshoi (250/h Mpc simulation box, 1/h kpc resolution) and MultiDark Run1 simulation (MDR1, or BigBolshoi, 1000/h Mpc simulation box, 7/h kpc resolution). The extraction methods for halos/subhalos from the raw simulation data, and how this data is structured in the database are explained in this paper. With the first data release, users get full access to halo/subhalo catalogs, various profiles of the halos at redshifts z=0-15, and raw dark matter data for one time-step of the Bolshoi and four time-steps of the MultiDark simulation. Later releases will also include galaxy mock catalogs and additional merging trees for both simulations as well as new large volume simulations with high resolution. This project is further proof of the viability to store and present complex data using relational database technology. We encourage other simulators to publish their results in a similar manner.Comment: 28 pages, 9 figures, submitted to New Astronom

Finite temperature crossover from a crystalline to a cluster phase for a confined finite chain of ions

Employing Monte-Carlo simulation techniques we investigate the statistical properties of equally charged particles confined in a one-dimensional box trap and detect a crossover from a crystalline to a cluster phase with increasing temperature. The corresponding transition temperature depends separately on the number of particles N and the box size L, implying non-extensivity due to the long-range character of the interactions. The probability density of the spacing between the particles exhibits at low temperatures an accumulation of discrete peaks with an overall asymmetric shape. In the vicinity of the transition temperature it is of a Gaussian form whereas in the high temperature regime an exponential decay is observed. The high temperature behaviour shows a cluster phase with a mean cluster size that first increases with the temperature and then saturates. The crossover is clearly identifiable also in the non-linear behaviour of the heat capacity with varying temperature. The influence of the trapping potential on the observed results as well as possible experimental realizations are briefly addressed.Comment: 12 pages, 13 figure

Reionization and Cosmology with 21 cm Fluctuations

Measurement of the spatial distribution of neutral hydrogen via the redshifted 21 cm line promises to revolutionize our knowledge of the epoch of reionization and the first galaxies, and may provide a powerful new tool for observational cosmology from redshifts 1<z<4 . In this review we discuss recent advances in our theoretical understanding of the epoch of reionization (EoR), the application of 21 cm tomography to cosmology and measurements of the dark energy equation of state after reionization, and the instrumentation and observational techniques shared by 21 cm EoR and post reionization cosmology machines. We place particular emphasis on the expected signal and observational capabilities of first generation 21 cm fluctuation instruments.Comment: Invited review for Annual Review of Astronomy and Astrophysics (2010 volume

Electronic Structure Shift of Deep Nanoscale Silicon by SiO2_2- vs. Si3_3N4_4-Embedding as Alternative to Impurity Doping

Conventional impurity doping of deep nanoscale silicon (dns-Si) used in ultra large scale integration (ULSI) faces serious challenges below the 14 nm technology node. We report on a new fundamental effect in theory and experiment, namely the electronic structure of dns-Si experiencing energy offsets of ca. 1 eV as a function of SiO$_2$- vs. Si$_3$N$_4$-embedding with a few monolayers (MLs). An interface charge transfer (ICT) from dns-Si specific to the anion type of the dielectric is at the core of this effect and arguably nested in quantum-chemical properties of oxygen (O) and nitrogen (N) vs. Si. We investigate the size up to which this energy offset defines the electronic structure of dns-Si by density functional theory (DFT), considering interface orientation, embedding layer thickness, and approximants featuring two Si nanocrystals (NCs); one embedded in SiO$_2$ and the other in Si$_3$N$_4$. Working with synchrotron ultraviolet photoelectron spectroscopy (UPS), we use SiO$_2$- vs. Si$_3$N$_4$-embedded Si nanowells (NWells) to obtain their energy of the top valence band states. These results confirm our theoretical findings and gauge an analytic model for projecting maximum dns-Si sizes for NCs, nanowires (NWires) and NWells where the energy offset reaches full scale, yielding to a clear preference for electrons or holes as majority carriers in dns-Si. Our findings can replace impurity doping for n/p-type dns-Si as used in ultra-low power electronics and ULSI, eliminating dopant-related issues such as inelastic carrier scattering, thermal ionization, clustering, out-diffusion and defect generation. As far as majority carrier preference is concerned, the elimination of those issues effectively shifts the lower size limit of Si-based ULSI devices to the crystalization limit of Si of ca. 1.5 nm and enables them to work also under cryogenic conditions.Comment: 14 pages, 17 Figures with a total 44 graph

Fuzzy Set Methods for Object Recognition in Space Applications

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms

Semi-analytic Simulations of Galactic Winds: Volume Filling Factor, Ejection of Metals and Parameter Study

We present a semi-analytic treatment of galactic winds within high resolution, large scale cosmological N-body simulations of a LCDM Universe. The evolution of winds is investigated by following the expansion of supernova driven superbubbles around the several hundred thousand galaxies that form in an approximately spherical region of space with diameter 52 Mpc/h and mean density close to the mean density of the Universe. We focus our attention on the impact of winds on the diffuse intergalactic medium. Initial conditions for mass loss at the base of winds are taken from Shu, Mo and Mao (2003). Results are presented for the volume filling factor and the mass fraction of the IGM affected by winds and their dependence on the model parameters is carefully investigated. The mass loading efficiency of bubbles is a key factor to determine the evolution of winds and their global impact on the IGM: the higher the mass loading, the later the IGM is enriched with metals. Galaxies with 10^9 < M_* < 10^10 M_sun are responsible for most of the metals ejected into the IGM at z=3, while galaxies with M_* < 10^9 M_sun give a non negligible contribution only at higher redshifts, when larger galaxies have not yet assembled. We find a higher mean IGM metallicity than Lyalpha forest observations suggest and we argue that the discrepancy may be explained by the high temperatures of a large fraction of the metals in winds, which may not leave detectable imprints in absorption in the Lyalpha forest.Comment: 18 pages, 15 figures. Major changes in the model. Manuscript with high resolution figures available upon request. MNRAS in pres

DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN-based methods are becoming increasingly popular in Cosmology. Cosmological data often comes as spherical maps, which make the use of the traditional CNNs more complicated. The commonly used pixelization scheme for spherical maps is the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere. The spherical CNN is constructed by representing the sphere as a graph. Graphs are versatile data structures that can act as a discrete representation of a continuous manifold. Using the graph-based representation, we define many of the standard CNN operations, such as convolution and pooling. With filters restricted to being radial, our convolutions are equivariant to rotation on the sphere, and DeepSphere can be made invariant or equivariant to rotation. This way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix sampling of the sphere. This approach is computationally more efficient than using spherical harmonics to perform convolutions. We demonstrate the method on a classification problem of weak lensing mass maps from two cosmological models and compare the performance of the CNN with that of two baseline classifiers. The results show that the performance of DeepSphere is always superior or equal to both of these baselines. For high noise levels and for data covering only a smaller fraction of the sphere, DeepSphere achieves typically 10% better classification accuracy than those baselines. Finally, we show how learned filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other author