The REFLEX Galaxy Cluster Survey VII: Omega_m and sigma_8 from cluster abundance and large-scale clustering

For the first time the large-scale clustering and the mean abundance of galaxy clusters are analysed simultaneously to get precise constraints on the normalized cosmic matter density $\Omega_m$ and the linear theory RMS fluctuations in mass $\sigma_8$. A self-consistent likelihood analysis is described which combines, in a natural and optimal manner, a battery of sensitive cosmological tests where observational data are represented by the (Karhunen-Lo\'{e}ve) eigenvectors of the sample correlation matrix. This method breaks the degeneracy between $\Omega_m$ and $\sigma_8$. The cosmological tests are performed with the ROSAT ESO Flux-Limited X-ray (REFLEX) cluster sample. The computations assume cosmologically flat geometries and a non-evolving cluster population mainly over the redshift range $0<z<0.3$. The REFLEX sample gives the cosmological constraints and their $1\sigma$ random errors of $\Omega_m = 0.341 ^{+0.031}_{-0.029}$ and $\sigma_8 = 0.711 ^{+0.039}_{-0.031}$. Possible systematic errors are evaluated by estimating the effects of uncertainties in the value of the Hubble constant, the baryon density, the spectral slope of the initial scalar fluctuations, the mass/X-ray luminosity relation and its intrinsic scatter, the biasing scheme, and the cluster mass density profile. All these contributions sum up to total systematic errors of $\sigma_{\Omega_m}=^{+0.087}_{-0.071}$ and $\sigma_{\sigma_8}=^{+0.120}_{-0.162}$.Comment: 10 pages, 7 figures, accepted for publication in Astronomy and Astrophysic

Bayesian computation via empirical likelihood

Approximate Bayesian computation (ABC) has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the ABC parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The BCel algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.Comment: 21 pages, 12 figures, revised version of the previous version with a new titl

Goodness-of-Fit Test: Khmaladze Transformation vs Empirical Likelihood

This paper compares two asymptotic distribution free methods for goodness-of-fit test of one sample of location-scale family: Khmaladze transformation and empirical likelihood methods. The comparison is made from the perspective of empirical level and power obtained from simulations. When testing for normal and logistic null distributions, we try various alternative distributions and find that Khmaladze transformation method has better power in most cases. R-package which was used for the simulation is available online. See section 5 for the detail