Constrained realizations of Gaussian fields - Reconstruction of the large-scale structure
1993; IOP Publishing; Volume: 415; Linguagem: Inglês
10.1086/187019
ISSN1538-4357
Autores Tópico(s)Cosmology and Gravitation Theories
Resumoview Abstract Citations (38) References (7) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Constrained Realizations of Gaussian Fields: Reconstruction of the Large-Scale Structure Ganon, Galit ; Hoffman, Yehuda Abstract The method of constrained realization (CR) of Gaussian random fields is applied to reconstruct our 'local' universe. A large observational data set is sampled and used as constraints imposed on realizations of an assumed primordial Gaussian perturbation field. To illustrate the method, the velocity potential as obtained by the POTENT algorithm from the observed velocity field is sampled at 181 different positions within a sphere of 40/h Mpc radius around us. Numerical realizations of the standard cold dark matter (CDM) model are constructed to yield the actual sampled values. These realizations do reconstruct the density perturbation field of the nearby universe. With only 181 constraints, the CR algorithm recovers the main features of POTENT's density field and, in particular, the Great Attractor region. The 12/h Mpc smoothed potential, which depends on the very long wavelengths of the underlying perturbation field, is used to constrain high-resolution (5/h Mpc smoothing) realizations. Thus, given an assumed model, high-resolution fields are created subject to low-resolution data. The method is easily applicable to the general case where any variable which depends linearly on the Gaussian field can be used to set the constraints. Publication: The Astrophysical Journal Pub Date: September 1993 DOI: 10.1086/187019 Bibcode: 1993ApJ...415L...5G Keywords: Dark Matter; Normal Density Functions; Perturbation Theory; Universe; Strange Attractors; Velocity Distribution; Astrophysics; COSMOLOGY: LARGE-SCALE STRUCTURE OF UNIVERSE full text sources ADS | data products SIMBAD (1)
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