257 research outputs found

    Resummed Perturbation Theory of Galaxy Clustering

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    The relationship between observed tracers such as galaxies and the underlying dark matter distribution is crucial in extracting cosmological information. As the linear bias model breaks down at quasi-linear scales, the standard perturbative approach of the nonlinear Eulerian bias model (EBM) is not accurate enough in describing galaxy clustering. In this paper, we discuss such a model in the context of resummed perturbation theory, and further generalize it to incorporate the subsequent gravitational evolution by combining with a Lagrangian description of galaxies' motion. The multipoint propagators we constructed for such model also exhibit exponential damping similar to their dark matter counterparts, therefore the convergence property of statistics built upon these quantities is improved. This is achieved by applying both Eulerian and Lagrangian resummation techniques of dark matter field developed in recent years. As inherited from the Lagrangian description of galaxy density evolution, our approach automatically incorporates the non-locality induced by gravitational evolution after the formation of the tracer, and also allows us to include a continuous galaxy formation history by temporally weighted-averaging relevant quantities with the galaxy formation rate.Comment: 19 pages, 18 figures, submitted to PR

    Science in an Exponential World

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    The amount of scientific information is doubling every year. This exponential growth is fundamentally changing every aspect of the scientific process – the collection, analysis and dissemination of scientific information. Our traditional paradigm for scientific publishing assumes a linear world, where the number of journals and articles remains approximately constant. The talk presents the challenges of this new paradigm and shows examples of how some disciplines are trying to cope with the data avalanche. In astronomy, the Virtual Observatory is emerging as a way to do astronomy in the 21st century. Other disciplines are also in the process of creating their own Virtual Observatories, on every imaginable scale of the physical world. We will discuss how long this exponential growth can continue

    Searchable Sky Coverage of Astronomical Observations: Footprints and Exposures

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    Sky coverage is one of the most important pieces of information about astronomical observations. We discuss possible representations, and present algorithms to create and manipulate shapes consisting of generalized spherical polygons with arbitrary complexity and size on the celestial sphere. This shape specification integrates well with our Hierarchical Triangular Mesh indexing toolbox, whose performance and capabilities are enhanced by the advanced features presented here. Our portable implementation of the relevant spherical geometry routines comes with wrapper functions for database queries, which are currently being used within several scientific catalog archives including the Sloan Digital Sky Survey, the Galaxy Evolution Explorer and the Hubble Legacy Archive projects as well as the Footprint Service of the Virtual Observatory.Comment: 11 pages, 7 figures, submitted to PAS

    IVOA Recommendation: Simple Cone Search Version 1.03

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    This specification defines a simple query protocol for retrieving records from a catalog of astronomical sources. The query describes sky position and an angular distance, defining a cone on the sky. The response returns a list of astronomical sources from the catalog whose positions lie within the cone, formatted as a VOTable. This version of the specification is essentially a transcription of the original Cone Search specification in order to move it into the IVOA standardization process

    Data-Intensive Computing in the 21st Century

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    The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems
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