7,653 research outputs found
The Flatness of Mass-to-Light Ratio on Large Scales
It has been suggested that the mass-to-light () ratio of gravitationally
clustering objects is scale-independent on scales beyond galaxy clusters, and
may also be independent of the mass of the objects. In this paper, we show that
the scale behavior of ratio is closely related to the scaling of cosmic
structures larger than clusters. The scale dependence of the ratio can be
determined by comparing the observed scaling of richness function (RF) of
multi-scale identified objects with the model-predicted scaling of mass
function (MF) of large scale structures. Using the multi-scale identified
clusters from IRAS 1.2 Jy galaxy survey, we have made comparisons of the
observed RF scaling of IRAS -clusters with the MF scalings given by
simulations of three popular models SCDM, LCDM and OCDM. We find that, the M/L
ratio basically is scale-independent from the Abell radius up to about 24
Mpc, while it seems to show a slight, but systematical, increase over
this scale range. This result is weakly dependent on the cosmological
parameters.Comment: AAS Latex file, 8 pages+ 4 figures, accepted for publication in ApJ
On Gaussian Comparison Inequality and Its Application to Spectral Analysis of Large Random Matrices
Recently, Chernozhukov, Chetverikov, and Kato [Ann. Statist. 42 (2014)
1564--1597] developed a new Gaussian comparison inequality for approximating
the suprema of empirical processes. This paper exploits this technique to
devise sharp inference on spectra of large random matrices. In particular, we
show that two long-standing problems in random matrix theory can be solved: (i)
simple bootstrap inference on sample eigenvalues when true eigenvalues are
tied; (ii) conducting two-sample Roy's covariance test in high dimensions. To
establish the asymptotic results, a generalized -net argument
regarding the matrix rescaled spectral norm and several new empirical process
bounds are developed and of independent interest.Comment: to appear in Bernoull
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