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Jclub history » History » Version 6

Version 5 (Robert Suhada, 12/12/2012 01:08 PM) → Version 6/35 (Robert Suhada, 12/13/2012 09:08 AM)

h1. Archive of discussed papers

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h2. 14.12.12: 14.12.12 *Bias from gas inhomogeneities in the pressure profiles as measured from X-ray and SZ observations*

S. Khedekar, E. Churazov, A. Kravtsov, I. Zhuravleva, E. T. Lau, D. Nagai, R. Sunyaev
(Submitted on 14 Nov 2012)
X-ray observations of galaxy clusters provide emission measure weighted spectra, arising from a range of density and temperature fluctuations in the intra-cluster medium (ICM). This is fitted to a single temperature plasma emission model to provide an estimate of the gas density and temperature, which are sensitive to the gas inhomogeneities. Therefore, X-ray observations yield a potentially biased estimate of the thermal gas pressure, P_X. At the same time Sunyaev-Zeldovich (SZ) observations directly measure the integrated gas pressure, P_SZ. If the X-ray pressure profiles are strongly biased with respect to to the SZ, then one has the possibility to probe the gas inhomogeneities, even at scales unresolved by the current generation of telescopes. At the same time, a weak bias has implications for the use of mass proxies like Y_SZ and Y_X as cosmological probes. In this paper we investigate the dependence of the bias, P_X(r)/P_SZ(r)-1, on the characteristics of fluctuations in the ICM taking into account the correlation between temperature and density fluctuations. We made a simple prediction of the irreducible bias in idealised X-ray vs SZ observations using multi-temperature plasma emission model. We also provide a simple fitting form to estimate the bias given the distribution of fluctuations. Analysing a sample of 16 simulated clusters extracted from hydrodynamical simulations, we find that the median value of bias is within +/-3% within R_500, it decreases to - 5% at R_500 < r < 1.5R_500 and then rises back to ~0% at > 2R_500. The scatter of b_P(r) between individual relaxed clusters is small -- at the level of <0.03 within R_500, but turns significantly larger (0.25) and highly skewed at r > 1.5 R_500. Unrelaxed clusters display larger scatter (both from radius to radius and from cluster to cluster). Nevertheless, the bias remains within +/-20% within 0.8R_500 for all clusters.

http://arxiv.org/abs/1211.3358

Votes: *2*

h2. 05.12.12: *Order statistics applied to the most massive and most distant galaxy clusters*

Jean-Claude Waizmann, Stefano Ettori, Matthias Bartelmann
(Submitted on 22 Oct 2012)
In this work we present for the first time an analytic framework for calculating the individual and joint distributions of the n-th most massive or n-th highest redshift galaxy cluster for a given survey characteristic allowing to formulate LCDM exclusion criteria. We show that the cumulative distribution functions steepen with increasing order, giving them a higher constraining power with respect to the extreme value statistics. Additionally, we find that the order statistics in mass (being dominated by clusters at lower redshifts) is sensitive to the matter density and the normalisation of the matter fluctuations, whereas the order statistics in redshift is particularly sensitive to the geometric evolution of the Universe. For a fixed cosmology, both order statistics are efficient probes of the functional shape of the mass function at the high mass end. To allow a quick assessment of both order statistics, we provide fits as a function of the survey area that allow percentile estimation with an accuracy better than two per cent. Furthermore, we discuss the joint distributions in the two-dimensional case for different combinations of order.
Having introduced the theory, we apply the order statistical analysis to the SPT massive cluster sample and MCXC catalogue and find that the ten most massive clusters in the sample are consistent with LCDM and the Tinker mass function. In turn, by assuming the LCDM reference cosmology, order statistics can also be utilised for consistency checks of the completeness of the observed sample and of the modelling of the survey selection function. [abridged]

http://arxiv.org/abs/1210.6021
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