期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 409, 期 1, 页码 355-370出版社
OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2010.17313.x
关键词
methods: data analysis; methods: numerical; cosmology: observations; large-scale structure of Universe
资金
- Transregional Collaborative Research Centre TRR 33 - The Dark Universe
- Alfred P. Sloan Foundation
- American Museum of Natural History
- Astrophysical Institute Potsdam
- University of Basel
- University of Cambridge
- Case Western Reserve University
- University of Chicago
- Drexel University
- Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University
- Joint Institute for Nuclear Astrophysics
- Kavli Institute for Particle Astrophysics and Cosmology
- Korean Scientist Group
- Chinese Academy of Sciences (LAMOST)
- Los Alamos National Laboratory
- Max-Planck-Institute for Astronomy (MPIA)
- Max-Planck-Institute for Astrophysics (MPA)
- New Mexico State University
- Ohio State University
- University of Pittsburgh
- University of Portsmouth
- Princeton University
- United States Naval Observatory
- University of Washington
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
In this work, we present the first non-linear, non-Gaussian full Bayesian large-scale structure analysis of the cosmic density field conducted so far. The density inference is based on the Sloan Digital Sky Survey (SDSS) Data Release 7, which covers the northern galactic cap. We employ a novel Bayesian sampling algorithm, which enables us to explore the extremely high dimensional non-Gaussian, non-linear lognormal Poissonian posterior of the three-dimensional density field conditional on the data. These techniques are efficiently implemented in the Hamiltonian Density Estimation and Sampling (HADES) computer algorithm and permit the precise recovery of poorly sampled objects and non-linear density fields. The non-linear density inference is performed on a 750-Mpc cube with roughly 3-Mpc grid resolution, while accounting for systematic effects, introduced by survey geometry and selection function of the SDSS, and the correct treatment of a Poissonian shot noise contribution. Our high-resolution results represent remarkably well the cosmic web structure of the cosmic density field. Filaments, voids and clusters are clearly visible. Further, we also conduct a dynamical web classification and estimate the web-type posterior distribution conditional on the SDSS data.
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