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Register a new userCombination of cluster number counts and two-point correlations: Validation on Mock Dark Energy Survey
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We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy--cluster cross-correlations, cluster auto-correlations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year One (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES Y1 experiment. We compare the $chi^2$ values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of a Dark Energy Survey Year 1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalog-level simulations.
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Higher-order, non-Gaussian aspects of the large-scale structure carry valuable information on structure formation and cosmology, which is complementary to second-order statistics. In this work we measure second- and third-order weak-lensing aperture-mass moments from CFHTLenS and combine those with CMB anisotropy probes. The third moment is measured with a significance of $2sigma$. The combined constraint on $Sigma_8 = sigma_8 (Omega_{rm m}/0.27)^alpha$ is improved by 10%, in comparison to the second-order only, and the allowed ranges for $Omega_{rm m}$ and $sigma_8$ are substantially reduced. Including general triangles of the lensing bispectrum yields tighter constraints compared to probing mainly equilateral triangles. Second- and third-order CFHTLenS lensing measurements improve Planck CMB constraints on $Omega_{rm m}$ and $sigma_8$ by 26% for flat $Lambda$CDM. For a model with free curvature, the joint CFHTLenS-Planck result is $Omega_{rm m} = 0.28 pm 0.02$ (68% confidence), which is an improvement of 43% compared to Planck alone. We test how our results are potentially subject to three astrophysical sources of contamination: source-lens clustering, the intrinsic alignment of galaxy shapes, and baryonic effects. We explore future limitations of the cosmological use of third-order weak lensing, such as the nonlinear model and the Gaussianity of the likelihood function.
We present three independent catalogs of point-sources extracted from SPIRE images at 250, 350, and 500 micron as a part of the Herschel Virgo Cluster Survey (HeViCS). The source positions are determined by estimating the likelihood to be a real source for each peak on the maps and the flux densities are estimated using the sourceExtractorTimeline, a timeline-based point source fitter. Afterwards, each source is subtracted from the maps, removing a Gaussian function in every position with the full width half maximum equal to that estimated in sourceExtractorTimeline. This procedure improves the robustness of our algorithm in terms of source identification. The HeViCS catalogs contain about 52000, 42200, and 18700 sources selected at 250, 350, and 500 micron above 3sigma and are ~ 75%, 62%, and 50% complete at flux densities of 20 mJy at 250, 350, 500 micron, respectively. We then measured source number counts at 250, 350, and 500 micron and we also cross-correlated the catalogs with the Sloan Digital Sky Survey to investigate the redshift distribution of the nearby sources. From this cross-correlation, we select ~2000 sources with reliable fluxes and a high signal-to-noise ratio, finding an average redshift z~0.3+/-0.22. The number counts at 250, 350, and 500 micron show an increase in the slope below 200 mJy, indicating a strong evolution in number of density for galaxies at these fluxes. In general, models tend to overpredict the counts at brighter flux densities, underlying the importance of studying the Rayleigh-Jeans part of the spectral energy distribution to refine the theoretical recipes of the models. Our iterative method for source identification allowed the detection of a family of 500 micron sources that are not foreground objects belonging to Virgo and not found in other catalogs.
The Swift AGN and Cluster Survey (SACS) uses 125 deg^2 of Swift XRT serendipitous fields with variable depths surrounding gamma-ray bursts to provide a medium depth (4e-15 erg/s/cm^2) and area survey filling the gap between deep, narrow Chandra/XMM-Newton surveys and wide, shallow ROSAT surveys. Here we present a catalog of 22,563 point sources and 442 extended sources and examine the number counts of the AGN and galaxy cluster populations. SACS provides excellent constraints on the AGN number counts at the bright end with negligible uncertainties due to cosmic variance, and these constraints are consistent with previous measurements. We use Wise mid-infrared (MIR) colors to classify the sources. For AGN we can roughly separate the point sources into MIR-red and MIR-blue AGN, finding roughly equal numbers of each type in the soft X-ray band (0.5-2 keV), but fewer MIR-blue sources in the hard X-ray band (2-8 keV). The cluster number counts, with 5% uncertainties from cosmic variance, are also consistent with previous surveys but span a much larger continuous flux range. Deep optical or IR follow-up observations of this cluster sample will significantly increase the number of higher redshift (z > 0.5) X-ray-selected clusters.
Context. We present an estimation of cosmological parameters with clusters of galaxies. Aims. We constrain the $Omega_m$, $sigma_8$, and $w$ parameters from a stand-alone sample of X-ray clusters detected in the 50 deg$^2$ XMM-XXL survey with a well-defined selection function. Methods. We analyse the redshift distribution of a sample comprising 178 high S/N clusters out to a redshift of unity. The cluster sample scaling relations are determined in a self-consistent manner. Results. In a lambda cold dark matter ($Lambda$CDM) model, the cosmology favoured by the XXL clusters compares well with results derived from the Planck S-Z clusters for a totally different sample (mass/redshift range, selection biases, and scaling relations). However, with this preliminary sample and current mass calibration uncertainty, we find no inconsistency with the Planck CMB cosmology. If we relax the $w$ parameter, the Planck CMB uncertainties increase by a factor of $sim$10 and become comparable with those from XXL clusters. Combining the two probes allows us to put constraints on $Omega_m$=0.316$pm$0.060, $sigma_8$=0.814$pm$0.054, and $w$=-1.02$pm$0.20. Conclusions. This first self-consistent cosmological analysis of a sample of serendipitous XMM clusters already provides interesting insights into the constraining power of the XXL survey. Subsequent analysis will use a larger sample extending to lower confidence detections and include additional observable information, potentially improving posterior uncertainties by roughly a factor of 3.
Sunyaev-Zeldovich (SZ) surveys are promising probes of cosmology - in particular for Dark Energy (DE) -, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects and maximum likelihood estimators may present biases for such sample sizes. In this work we use the Monte Carlo approach to determine the presence of bias on cosmological parameter estimators from cluster abundance as a function of the area and depth of the survey, and the number of cosmological parameters fitted. Assuming perfect knowledge of mass and redshift some estimators have non-negligible biases. For example, the bias of $sigma_8$ corresponds to about $40%$ of its statistical error bar when fitted together with $Omega_c$ and $w_0$. Including a SZ mass-observable relation decreases the relevance of the bias, for the typical sizes of current surveys. The biases become negligible when combining the SZ data with other cosmological probes. However, we show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the South Pole Telescope (SPT) sensitivity. The results of this work validate the use of the current maximum likelihood methods for present SZ surveys, but highlight the need for further studies for upcoming experiments. [abridged]
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