We use cosmological gas dynamic simulations to investigate the accuracy of galaxy cluster mass estimates based on X-ray observations. The experiments follow the formation of clusters in different cosmological models and include the effects of gravity, pressure gradients, and hydrodynamical shocks. A subset of our ensemble also allows for feedback of mass and energy from galactic winds into the intracluster medium. We find that mass estimates based on the hydrostatic, isothermal beta-model are remarkably accurate when evaluated at radii where the cluster mean density is between 500-2500 times the critical density. Applied to 174 artificial ROSAT images constructed from the simulations, the distribution of the estimated-to-true mass ratio is nearly unbiased and has a standard deviation of 14-29%. The scatter can be considerably reduced (to 8-15%) by using an alternative mass estimator that exploits the tightness of the mass-temperature relation found in the simulations. The improvement over beta-model estimates is due to the elimination of the variance contributed by the gas outer slope parameter. We discuss these findings and their implications for recent measurements of cluster baryon fractions.Comment: TeX, 24p; 11 Postscript figs. Submitted to the Astrophysical Journa

A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, todays denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively employ a generic denoiser in a CS reconstruction algorithm? In response, we develop an extension of the approximate message passing (AMP) framework, called Denoising-based AMP (D-AMP), that can integrate a wide class of denoisers within its iterations. We demonstrate that, when used with a high performance denoiser for natural images, D-AMP offers state-of-the-art CS recovery performance while operating tens of times faster than competing methods. We explain the exceptional performance of D-AMP by analyzing some of its theoretical features. A key element in D-AMP is the use of an appropriate Onsager correction term in its iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove.1 Note that for notational simplicity in our current derivations and algorithms we restrict A to be in R mˆn . However, an extension to C mˆn is also possible.2 A QQplot is a visual inspection tool for checking the Gaussianity of the data. In a QQplot, deviation from a straight line is an evidence of non-Gaussianity.

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In modern hierarchical theories of structure formation, rich clusters of galaxies form at the vertices of a weblike distribution of matter, with filaments emanating from them to large distances and with smaller objects forming and draining in along these filaments. The amount of mass contained in structure near the cluster can be comparable to the collapsed mass of the cluster itself. As the lensing kernel is quite broad along the line of sight around cluster lenses with typical redshifts near z=0.5, structures many Mpc away from the cluster are essentially at the same location as the cluster itself, when considering their effect on the cluster's weak lensing signal. We use large-scale numerical simulations of structure formation in a Lambda-dominated cold dark matter model to quantify the effect that large-scale structure near clusters has upon the cluster masses deduced from weak lensing analysis. A correction for the scatter in possible observed lensing masses should be included when interpreting mass functions from weak lensing surveys.Comment: 14 pages, 11 figures. LaTeX2e, uses emulateapj.sty and onecolfloat.st