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Register a new userComparison of Pressure Profiles of Massive Relaxed Galaxy Clusters using Sunyaev-Zeldovich and X-ray Data
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Massimiliano (Max) Bonamente
Publication date
2011
fields
Physics
and research's language is
English
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We present Sunyaev-Zeldovich (SZ) effect observations of a sample of 25 massive relaxed galaxy clusters observed with the Sunyaev-Zeldovich Array (SZA), an 8-element interferometer that is part of the Combined Array for Research in Millimeter-wave Astronomy (CARMA). We perform an analysis of new SZA data and archival Chandra observations of this sample to investigate the integrated pressure -- a proxy for cluster mass -- determined from X-ray and SZ observations, two independent probes of the intra-cluster medium. This analysis makes use of a model for the intra-cluster medium introduced by Bulbul (2010) which can be applied simultaneously to SZ and X-ray data. With this model, we estimate the pressure profile for each cluster using a joint analysis of the SZ and X-ray data, and using the SZ data alone. We find that the integrated pressures measured from X-ray and SZ data are consistent. This conclusion is in agreement with recent results obtained using WMAP and Planck data, confirming that SZ and X-ray observations of massive clusters detect the same amount of thermal pressure from the intra-cluster medium. To test for possible biases introduced by our choice of model, we also fit the SZ data using the universal pressure profile proposed by Arnaud (2010), and find consistency between the two models out to r500 in the pressure profiles and integrated pressures.
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We describe Sunyaev-Zeldovich (SZ) effect measurements and analysis of the intracluster medium (ICM) pressure profiles of a set of 45 massive galaxy clusters imaged using Bolocam at the Caltech Submillimeter Observatory. We have used masses determined from Chandra X-ray observations to scale each clusters profile by the overdensity radius R500 and the mass-and-redshift-dependent normalization factor P500. We deproject the average pressure profile of our sample into 13 logarithmically spaced radial bins between 0.07R500 and 3.5R500. We find that a generalized Navarro, Frenk, and White (gNFW) profile describes our data with sufficient goodness-of-fit and best-fit parameters (C500, alpha, beta, gamma, P0 = 1.18, 0.86, 3.67, 0.67, 4.29). We also use the X-ray data to define cool-core and disturbed subsamples of clusters, and we constrain the average pressure profiles of each of these subsamples. We find that given the precision of our data the average pressure profiles of disturbed and cool-core clusters are consistent with one another at R>~0.15R500, with cool-core systems showing indications of higher pressure at R<~0.15R500. In addition, for the first time, we place simultaneous constraints on the mass scaling of cluster pressure profiles, their ensemble mean profile, and their radius-dependent intrinsic scatter between 0.1R500 and 2.0R500. The scatter among profiles is minimized at radii between ~0.2R500 and ~0.5R500, with a value of ~20%. The best-fit mass scaling has a power-law slope of 0.49, which is shallower than the nominal prediction of 2/3 from self-similar hydrostatic equilibrium models. These results for the intrinsic scatter and mass scaling are largely consistent with previous analyses, most of which have relied heavily on X-ray derived pressures of clusters at significantly lower masses and redshifts compared to our sample.
Taking advantage of the all-sky coverage and broad frequency range of the Planck satellite, we study the Sunyaev-Zeldovich (SZ) and pressure profiles of 62 nearby massive clusters detected at high significance in the 14-month nominal survey. Careful reconstruction of the SZ signal indicates that most clusters are individually detected at least out to R500. By stacking the radial profiles, we have statistically detected the radial SZ signal out to 3 x R500, i.e., at a density contrast of about 50-100, though the dispersion about the mean profile dominates the statistical errors across the whole radial range. Our measurement is fully consistent with previous Planck results on integrated SZ fluxes, further strengthening the agreement between SZ and X-ray measurements inside R500. Correcting for the effects of the Planck beam, we have calculated the corresponding pressure profiles. This new constraint from SZ measurements is consistent with the X-ray constraints from XMM-Newton in the region in which the profiles overlap (i.e., [0.1-1]R500), and is in fairly good agreement with theoretical predictions within the expected dispersion. At larger radii the average pressure profile is slightly flatter than most predictions from numerical simulations. Combining the SZ and X-ray observed profiles into a joint fit to a generalised pressure profile gives best-fit parameters [P0, c500, gamma, alpha, beta] = [6.41, 1.81, 0.31, 1.33, 4.13]. Using a reasonable hypothesis for the gas temperature in the cluster outskirts we reconstruct from our stacked pressure profile the gas mass fraction profile out to 3 x R500. Within the temperature driven uncertainties, our Planck constraints are compatible with the cosmic baryon fraction and expected gas fraction in halos.
We used optical imaging and spectroscopic data to derive substructure estimates for local Universe ($z < 0.11$) galaxy clusters from two different samples. The first was selected through the Sunyaev-Zeldovich (SZ) effect by the Planck satellite and the second is an X-ray selected sample. In agreement to X-ray substructure estimates we found that the SZ systems have a larger fraction of substructure than the X-ray clusters. We have also found evidence that the higher mass regime of the SZ clusters, compared to the X-ray sample, explains the larger fraction of disturbed objects in the Planck data. Although we detect a redshift evolution in the substructure fraction, it is not sufficient to explain the different results between the higher-z SZ sample and the X-ray one. We have also verified a good agreement ($sim$60$%$) between the optical and X-ray substructure estimates. However, the best level of agreement is given by the substructure classification given by measures based on the brightest cluster galaxy (BCG), either the BCG$-$X-ray centroid offset, or the magnitude gap between the first and second BCGs. We advocate the use of those two parameters as the most reliable and cheap way to assess cluster dynamical state. We recommend an offset cut of $sim$0.01$times$R$_{500}$ to separate relaxed and disturbed clusters. Regarding the magnitude gap the separation can be done at $Delta m_{12} = 1.0$. The central galaxy paradigm (CGP) may not be valid for $sim$20$%$ of relaxed massive clusters. This fraction increases to $sim$60$%$ for disturbed systems.
Galaxy clusters, the most massive collapsed structures, have been routinely used to determine cosmological parameters. When using clusters for cosmology, the crucial assumption is that they are relaxed. However, subarcminute resolution Sunyaev-Zeldovich (SZ) effect images compared with high resolution X-ray images of some clusters show significant offsets between the two peaks. We have carried out self-consistent N-body/hydrodynamical simulations of merging galaxy clusters using FLASH to study these offsets quantitatively. We have found that significant displacements result between the SZ and X-ray peaks for large relative velocities for all masses used in our simulations as long as the impact parameters were about 100-250 kpc. Our results suggest that the SZ peak coincides with the peak in the pressure times the line-of-sight characteristic length and not the pressure maximum (as it would for clusters in equilibrium). The peak in the X-ray emission, as expected, coincides with the density maximum of the main cluster. As a consequence, the morphology of the SZ signal and therefore the offset between the SZ and X-ray peaks change with viewing angle. As an application, we compare the morphologies of our simulated images to observed SZ and X-ray images and mass surface densities derived from weak lensing observations of the merging galaxy cluster CL0152-1357. We find that a large relative velocity of 4800 km/s is necessary to explain these observations. We conclude that an analysis of the morphologies of multi-frequency observations of merging clusters can be used to put meaningful constraints on the initial parameters of the progenitors.
Galaxy clusters identified from the Sunyaev Zeldovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show results for a `combined identifier. We apply the methods to simulated millimeter maps for several observing frequencies for an SPT-3G-like survey. There are some key differences between the methods. The MF method requires image pre-processing to remove point sources and a model for the noise, while the CNN method requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes as input, cutout images of the sky. Specifically, we use the CNN to classify whether or not an 8 arcmin $times$ 8 arcmin cutout of the sky contains a cluster. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some of which have SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness of the MF alone are both 0.61, assuming a standard detection threshold. The purity and completeness of the CNN alone are 0.59 and 0.61. The combined classification method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many lower signal-to-noise clusters.
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