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Register a new userConstraints on Omega_m, Omega_L, and Sigma_8, from Galaxy Cluster Redshift Distributions
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We show that the counts of galaxy clusters in future deep cluster surveys can place strong constraints on the matter density, Omega_m, the vacuum energy density, Omega_L, and the normalization of the matter power spectrum, sigma_8. Degeneracies between these parameters are different from those in studies of either high--redshift type Ia Supernovae (SNe), or cosmic microwave background (CMB) anisotropies. Using a mass threshold for cluster detection expected to be typical for upcoming SZE surveys, we find that constraints on Omega_m and sigma_8 at the level of roughly 5% or better can be expected, assuming redshift information is known at least to z=0.5 and in the absence of significant systematic errors. Without information past this redshift, Omega_L is constrained to 25%. With complete redshift information, deep (M_{lim}= 10^{14}h^{-1}{M_sun}), relatively small solid angle (roughly 12 {deg}^2) surveys can further constrain Omega_L to an accuracy of 15%, while large solid angle surveys with ground-based large-format bolometer arrays could measure Omega_L to a precision of 4% or better.
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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 $1sigma$ 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 $sigma_{sigma_8}=^{+0.120}_{-0.162}$.
We derive cosmological constraints on the matter density, om, and the amplitude of fluctuations, sig, using $mathtt{GalWCat19}$, a catalog of 1800 galaxy clusters we identified in the Sloan Digital Sky Survey-DR13 spectroscopic data set using our GalWeight technique to determine cluster membership citep{Abdullah18,Abdullah19}. By analyzing a subsample of 756 clusters in a redshift range of $0.045leq z leq 0.125$ and virial masses of $Mgeq 0.8times10^{14}$ hm ~with mean redshift of $z = 0.085$, we obtain om ~$=0.310^{+0.023}_{-0.027} pm 0.041$ (systematic) and sig ~$=0.810^{+0.031}_{-0.036}pm 0.035$ (systematic), with a cluster normalization relation of $sigma_8= 0.43 Omega_m^{-0.55}$. There are several unique aspects to our approach: we use the largest spectroscopic data set currently available, and we assign membership using the GalWeight technique which we have shown to be very effective at simultaneously maximizing the number of {it{bona fide}} cluster members while minimizing the number of contaminating interlopers. Moreover, rather than employing scaling relations, we calculate cluster masses individually using the virial mass estimator. Since $mathtt{GalWCat19}$ is a low-redshift cluster catalog we do not need to make any assumptions about evolution either in cosmological parameters or in the properties of the clusters themselves. Our constraints on om ~and sig ~are consistent and very competitive with those obtained from non-cluster abundance cosmological probes such as Cosmic Microwave Background (CMB), Baryonic Acoustic Oscillation (BAO), and supernovae (SNe). The joint analysis of our cluster data with Planck18+BAO+Pantheon gives om ~$=0.315^{+0.013}_{-0.011}$ and sig ~$=0.810^{+0.011}_{-0.010}$.
We present a cosmic shear study from the Deep Lens Survey (DLS), a deep BVRz multi-band imaging survey of five 4 sq. degree fields with two National Optical Astronomy Observatory (NOAO) 4-meter telescopes at Kitt Peak and Cerro Tololo. For both telescopes, the change of the point-spread-function (PSF) shape across the focal plane is complicated, and the exposure-to-exposure variation of this position-dependent PSF change is significant. We overcome this challenge by modeling the PSF separately for individual exposures and CCDs with principal component analysis (PCA). We find that stacking these PSFs reproduces the final PSF pattern on the mosaic image with high fidelity, and the method successfully separates PSF-induced systematics from gravitational lensing effects. We calibrate our shears and estimate the errors, utilizing an image simulator, which generates sheared ground-based galaxy images from deep Hubble Space Telescope archival data with a realistic atmospheric turbulence model. For cosmological parameter constraints, we marginalize over shear calibration error, photometric redshift uncertainty, and the Hubble constant. We use cosmology-dependent covariances for the Markov Chain Monte Carlo analysis and find that the role of this varying covariance is critical in our parameter estimation. Our current non-tomographic analysis alone constrains the Omega_M-sigma_8 likelihood contour tightly, providing a joint constraint of Omega_M=0.262+-0.051 and sigma_8=0.868+-0.071. We expect that a future DLS weak-lensing tomographic study will further tighten these constraints because explicit treatment of the redshift dependence of cosmic shear more efficiently breaks the Omega_M-sigma_8 degeneracy. Combining the current results with the Wilkinson Microwave Anisotropy Probe 7-year (WMAP7) likelihood data, we obtain Omega_M=0.278+-0.018 and sigma_8=0.815+-0.020.
We generate mock galaxy catalogues for a grid of different cosmologies, using rescaled N-body simulations in tandem with a semi-analytic model run using consistent parameters. Because we predict the galaxy bias, rather than fitting it as a nuisance parameter, we obtain an almost pure constraint on sigma_8 by comparing the projected two-point correlation function we obtain to that from the SDSS. A systematic error arises because different semi-analytic modelling assumptions allow us to fit the r-band luminosity function equally well. Combining our estimate of the error from this source with the statistical error, we find sigma_8=0.97 +/- 0.06. We obtain consistent results if we use galaxy samples with a different magnitude threshold, or if we select galaxies by b_J-band rather than r-band luminosity and compare to data from the 2dFGRS. Our estimate for sigma_8 is higher than that obtained for other analyses of galaxy data alone, and we attempt to find the source of this difference. We note that in any case, galaxy clustering data provide a very stringent constraint on galaxy formation models.
We present a full description of the N-probability density function of the galaxy number density fluctuations. This N-pdf is given in terms, on the one hand, of the cold dark matter correlations and, on the other hand, of the galaxy bias parameter. The method relies on the assumption commonly adopted that the dark matter density fluctuations follow a local non-linear transformation of the initial energy density perturbations. The N-pdf of the galaxy number density fluctuations allows for an optimal estimation of the bias parameter (e.g., via maximum-likelihood estimation, or Bayesian inference if there exists any a priori information on the bias parameter), and of those parameters defining the dark matter correlations, in particular its amplitude ($sigma_8$). It also provides the proper framework to perform model selection between two competitive hypotheses. The parameters estimation capabilities of the N-pdf are proved by SDSS-like simulations (both ideal log-normal simulations and mocks obtained from Las Damas simulations), showing that our estimator is unbiased. We apply our formalism to the 7th release of the SDSS main sample (for a volume-limited subset with absolute magnitudes $M_r leq -20$). We obtain $hat{b} = 1.193 pm 0.074$ and $hat{sigma_8} = 0.862 pm 0.080$, for galaxy number density fluctuations in cells of a size of $30h^{-1}$Mpc. Different model selection criteria show that galaxy biasing is clearly favoured.
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