排序方式： 共有97条查询结果，搜索用时 62 毫秒
81.
Nicholas Cross Simon P. Driver Warrick Couch Carlton M. Baugh Joss Bland-Hawthorn Terry Bridges Russell Cannon Shaun Cole Matthew Colless Chris Collins Gavin Dalton Kathryn Deeley Roberto De Propris George Efstathiou Richard S. Ellis Carlos S. Frenk Karl Glazebrook Carole Jackson Ofer Lahav Ian Lewis Stuart Lumsden Steve Maddox Darren Madgwick Stephen Moody Peder Norberg John A. Peacock Bruce A. Peterson Ian Price Mark Seaborne Will Sutherland Helen Tadros Keith Taylor 《Monthly notices of the Royal Astronomical Society》2001,324(4):825-841
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Edward Conway Steve Maddox Vivienne Wild John A. Peacock Ed Hawkins Peder Norberg Darren S. Madgwick Ivan K. Baldry Carlton M. Baugh Joss Bland-Hawthorn Terry Bridges Russell Cannon Shaun Cole Matthew Colless Chris Collins Warrick Couch Gavin Dalton Roberto De Propris Simon P. Driver George Efstathiou Richard S. Ellis Carlos S. Frenk Karl Glazebrook Carole Jackson Bryn Jones Ofer Lahav Ian Lewis Stuart Lumsden Will Percival Bruce A. Peterson Will Sutherland Keith Taylor 《Monthly notices of the Royal Astronomical Society》2005,356(2):456-474
We present an analysis of the relative bias between early- and late-type galaxies in the Two-degree Field Galaxy Redshift Survey (2dFGRS) – as defined by the η parameter of Madgwick et al., which quantifies the spectral type of galaxies in the survey. We calculate counts in cells for flux-limited samples of early- and late-type galaxies, using approximately cubical cells with sides ranging from 7 to 42 h −1 Mpc . We measure the variance of the counts in cells using the method of Efstathiou et al., which we find requires a correction for a finite volume effect equivalent to the integral constraint bias of the autocorrelation function. Using a maximum-likelihood technique we fit lognormal models to the one-point density distribution, and develop methods of dealing with biases in the recovered variances resulting from this technique. We then examine the joint density distribution function, f (δE , δL ) , and directly fit deterministic bias models to the joint counts in cells. We measure a linear relative bias of ≈1.3, which does not vary significantly with ℓ. A deterministic linear bias model is, however, a poor approximation to the data, especially on small scales (ℓ≤ 28 h −1 Mpc) where deterministic linear bias is excluded at high significance. A power-law bias model with index b 1 ≈ 0.75 is a significantly better fit to the data on all scales, although linear bias becomes consistent with the data for ℓ≳ 40 h −1 Mpc . 相似文献
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A.J. Benson C.S. Frenk C.M. Baugh S. Cole C.G. Lacey 《Monthly notices of the Royal Astronomical Society》2001,327(4):1041-1056
We follow the evolution of the galaxy population in a ΛCDM cosmology by means of high-resolution N -body simulations in which the formation of galaxies and their observable properties are calculated using a semi-analytic model. We display images of the spatial distribution of galaxies in the simulations that illustrate its evolution and provide a qualitative understanding of the processes responsible for the various biases that develop. We consider three specific statistical measures of clustering at and : the correlation length (in both real and redshift space) of galaxies of different luminosity, the morphology–density relation and the genus curve of the topology of galaxy isodensity surfaces. For galaxies with luminosity below L ∗, the correlation length depends very little on the luminosity of the sample, but for brighter galaxies it increases very rapidly, reaching values in excess of 10 h −1 Mpc. The 'accelerated' dynamical evolution experienced by galaxies in rich clusters, which is partly responsible for this effect, also results in a strong morphology–density relation. Remarkably, this relation is already well-established at . The genus curves of the galaxies are significantly different from the genus curves of the dark matter, however this is not a result of genuine topological differences but rather of the sparse sampling of the density field provided by galaxies. The predictions of our model at will be tested by forthcoming data from the 2dF and Sloan galaxy surveys, and those at by the DEEP and VIRMOS surveys. 相似文献
84.
Darren J. Croton Matthew Colless Enrique Gaztañaga Carlton M. Baugh Peder Norberg I. K. Baldry J. Bland-Hawthorn T. Bridges R. Cannon S. Cole C. Collins W. Couch G. Dalton R. De Propris S. P. Driver G. Efstathiou R. S. Ellis C. S. Frenk K. Glazebrook C. Jackson O. Lahav I. Lewis S. Lumsden S. Maddox D. Madgwick J. A. Peacock B. A. Peterson W. Sutherland K. Taylor 《Monthly notices of the Royal Astronomical Society》2004,352(3):828-836
85.
Will J. Percival Daniel Burkey Alan Heavens y Taylor Shaun Cole John A. Peacock Carlton M. Baugh Joss Bland-Hawthorn Terry Bridges Russell Cannon Matthew Colless Chris Collins Warrick Couch Gavin Dalton Roberto De Propris Simon P. Driver George Efstathiou Richard S. Ellis Carlos S. Frenk Karl Glazebrook Carole Jackson Ofer Lahav Ian Lewis Stuart Lumsden Steve Maddox Peder Norberg Bruce A. Peterson Will Sutherland Keith Taylor 《Monthly notices of the Royal Astronomical Society》2004,353(4):1201-1218
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CHEN Jun LI Chengming LI Zhilin Gold C M 《地球空间信息科学学报》2000,3(1):1-10
1 Overview of the original 9-inter-section modelThe spatial re1ations betWeen spatial entities areknown as important as the entities themselves. It istherefore very essential to know what poSSibIe spa-tial relationships are and how they can be deter-mined. The 9-intersection model is the most POpu-lar mathematical framework fOr formalizing spatialrelations and have been widely used in spatial querylanguages(EngenhOfer, l991; Clementinietal., l994;Mark et al., l995). Using this medel the t… 相似文献
90.
R. E. Angulo C. M. Baugh C. G. Lacey 《Monthly notices of the Royal Astronomical Society》2008,387(2):921-932
We use an extremely large volume (2.4 h −3 Gpc3 ) , high-resolution N -body simulation to measure the higher order clustering of dark matter haloes as a function of mass and internal structure. As a result of the large simulation volume and the use of a novel 'cross-moment' counts-in-cells technique which suppresses discreteness noise, we are able to measure the clustering of haloes corresponding to rarer peaks than was possible in previous studies; the rarest haloes for which we measure the variance are 100 times more clustered than the dark matter. We are able to extract, for the first time, halo bias parameters from linear up to fourth order. For all orders measured, we find that the bias parameters are a strong function of mass for haloes more massive than the characteristic mass M * . Currently, no theoretical model is able to reproduce this mass dependence closely. We find that the bias parameters also depend on the internal structure of the halo up to fourth order. For haloes more massive than M * , we find that the more concentrated haloes are more weakly clustered than the less concentrated ones. We see no dependence of clustering on concentration for haloes with masses M < M * ; this is contrary to the trend reported in the literature when segregating haloes by their formation time. Our results are insensitive to whether haloes are labelled by the total mass returned by the friends-of-friends group finder or by the mass of the most massive substructure. This implies that our conclusions are not an artefact of the particular choice of group finding algorithm. Our results will provide important input to theoretical models of galaxy clustering. 相似文献