The Effect of Corner Modes in the Initial Conditions of Cosmological Simulations

In view of future high-precision large-scale structure surveys, it is important to quantify the percent and subpercent level effects in cosmological N-body simulations from which theoretical predictions are drawn. One such effect involves deciding whether to zero all modes above the one-dimensional Nyquist frequency, the so-called “corner” modes, in the initial conditions. We investigate this effect by comparing power spectra, density distribution functions, halo mass functions, and halo profiles in simulations with and without these modes. For a simulation with a mass resolution of mp ~ 1011 -h M 1 , we find that at z > 6, the difference in the matter power spectrum is large at wavenumbers above ∼80% of kNy, reducing to below 2% at all scales by z ~ 3. Including corner modes results in a better match between low- and high-resolution simulations at wavenumbers around the Nyquist frequency of the low-resolution simulation, but the effect of the corner modes is smaller than the effect of particle discreteness. The differences in mass functions are 3% for the smallest halos at z = 6 for the mp ~ 1011 -h M 1 simulation, but we find no significant difference in the stacked profiles of well-resolved halos at z 6. Thus removing power at ∣k∣ > kNy in the initial conditions of cosmological simulations has a small effect on small scales and high redshifts, typically below a few percent

Distribution of Maximal Luminosity of Galaxies in the Sloan Digital Sky Survey

Extreme value statistics (EVS) is applied to the distribution of galaxy luminosities in the Sloan Digital Sky Survey (SDSS). We analyze the DR8 Main Galaxy Sample (MGS), as well as the Luminous Red Galaxies (LRG). Maximal luminosities are sampled from batches consisting of elongated pencil beams in the radial direction of sight. For the MGS, results suggest a small and positive tail index $\xi$, effectively ruling out the possibility of having a finite maximum cutoff luminosity, and implying that the luminosity distribution function may decay as a power law at the high luminosity end. Assuming, however, $\xi=0$, a non-parametric comparison of the maximal luminosities with the Fisher-Tippett-Gumbel distribution (limit distribution for variables distributed by the Schechter fit) indicates a good agreement provided uncertainties arising both from the finite batch size and from the batch size distribution are accounted for. For a volume limited sample of LRGs, results show that they can be described as being the extremes of a luminosity distribution with an exponentially decaying tail, provided the uncertainties related to batch-size distribution are taken care of

Limit on Tau Neutrino Mass from τ−→π−π+π−π0ντ\tau^{-}\to \pi^{-}\pi^{+}\pi^{-}\pi^{0}\nu_{\tau}

From a data sample of 29058 $\tau^\pm\to\pi^\pm\pi^+\pi^-\pi^0\nu_\tau$ decays observed in the CLEO detector we derive a 95% confidence upper limit on the tau neutrino mass of 28 MeV.Comment: 17 pages postscript, also available through http://w4.lns.cornell.edu/public/CLN

Spectroscopic target selection for the Sloan Digital Sky Survey: The luminous red galaxy sample

We describe the target selection and resulting properties of a spectroscopic sample of luminous red galaxies (LRGs) from the imaging data of the Sloan Digital Sky Survey (SDSS). These galaxies are selected on the basis of color and magnitude to yield a sample of luminous intrinsically red galaxies that extends fainter and farther than the main flux-limited portion of the SDSS galaxy spectroscopic sample. The sample is designed to impose a passively evolving luminosity and rest-frame color cut to a redshift of 0.38. Additional, yet more luminous red galaxies are included to a redshift of ∼0.5. Approximately 12 of these galaxies per square degree are targeted for spectroscopy, so the sample will number over 100,000 with the full survey. SDSS commissioning data indicate that the algorithm efficiently selects luminous (M*g ≈ - 21.4) red galaxies, that the spectroscopic success rate is very high, and that the resulting set of galaxies is approximately volume limited out to z = 0.38. When the SDSS is complete, the LRG spectroscopic sample will fill over 1 h-3 Gpc3 with an approximately homogeneous population of galaxies and will therefore be well suited to studies of large-scale structure and clusters out to z = 0.5

Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass

Cosmological NN-body simulations play a vital role in studying models for the evolution of the Universe. To compare to observations and make a scientific inference, statistic analysis on large simulation datasets, e.g., finding halos, obtaining multi-point correlation functions, is crucial. However, traditional in-memory methods for these tasks do not scale to the datasets that are forbiddingly large in modern simulations. Our prior paper (Liu et al., 2015) proposes memory-efficient streaming algorithms that can find the largest halos in a simulation with up to 109109 particles on a small server or desktop. However, this approach fails when directly scaling to larger datasets. This paper presents a robust streaming tool that leverages state-of-the-art techniques on GPU boosting, sampling, and parallel I/O, to significantly improve performance and scalability. Our rigorous analysis of the sketch parameters improves the previous results from finding the centers of the 103103 largest halos (Liu et al., 2015) to ∼104−105∼104−105, and reveals the trade-offs between memory, running time and number of halos. Our experiments show that our tool can scale to datasets with up to ∼1012∼1012 particles while using less than an hour of running time on a single GPU Nvidia GTX 1080

Trust networks on the semantic web

Abstract. The so-called "Web of Trust " is one of the ultimate goals of the Semantic Web. Research on the topic of trust in this domain has focused largely on digital signatures, certificates, and authentication. At the same time, there is a wealth of research into trust and social networks in the physical world. In this paper, we describe an approach for integrating the two to build a web of trust in a more social respect. This paper describes the applicability of social network analysis to the semantic web, particularly discussing the multi-dimensional networks that evolve from ontological trust specifications. As a demonstration of algorithms used to infer trust relationships, we present several tools that allow users to take advantage of trust metrics that use the network.

Optimization and Execution of Complex Scientific Queries over Uncorrelated Experimental Data

Abstract. Scientific experiments produce large volumes of data represented as complex objects that describe independent events such as particle collisions. Scientific analyses can be expressed as queries selecting objects that satisfy complex local conditions over properties of each object. The conditions include joins, aggregate functions, and numerical computations. Traditional query processing where data is loaded into a database does not perform well, since it takes time and space to load and index data. Therefore, we developed SQISLE to efficiently process in one pass large queries selecting complex objects from sources. Our contributions include runtime query optimization strategies, which during query execution collect runtime query statistics, reoptimize the query using collected statistics, and dynamically switch optimization strategies. Furthermore, performance is improved by query rewrites, temporary view materializations, and compile time evaluation of query fragments. We demonstrate that queries in SQISLE perform close to hard-coded C++ implementations of the same analyses.