Constraints on the Mass, Concentration, and Nonthermal Pressure Support of Six CLASH Clusters from a Joint Analysis of X-Ray, SZ, and Lensing Data

Abstract

We present a joint analysis of Chandra X-ray observations, Bolocam thermal Sunyaev-Zel'dovich (SZ) effect observations, Hubble Space Telescope (HST) strong-lensing data, and HST and Subaru Suprime-Cam weak-lensing data. The multiwavelength data set is used to constrain parametric models for the distribution of dark and baryonic matter in a sample of six massive galaxy clusters selected from the Cluster Lensing And Supernova survey with Hubble (CLASH). For five of the six clusters, the multiwavelength data set is well described by a relatively simple model that assumes spherical symmetry, hydrostatic equilibrium, and entirely thermal pressure support. The joint analysis yields considerably better constraints on the total mass and concentration of the clusters compared to analysis of any one data set individually. The resulting constraints are consistent with simulation-based predictions for the concentration-mass relation. The subsample of five galaxy clusters is used to place an upper limit on the fraction of pressure support in the intracluster medium (ICM) due to nonthermal processes, such as turbulence and bulk flow of the gas. We constrain the nonthermal pressure fraction at r 500c to be <0.11 at 95% confidence. This is in tension with state-of-the-art hydrodynamical simulations, which predict a nonthermal pressure fraction of ≈0.25 at r 500c for clusters of similar mass and redshift. This tension may be explained by the sample selection and/or our assumption of spherical symmetry.

The Physics of Galaxy Cluster Outskirts

“…A detailed comparison of the mass estimates ) also shows that X-COP hydrostatic masses agree with WL-based measurements for the same clusters within 10%, which supports this conclusion. In the CLASH sample, Siegel et al (2018) reached a similar conclusion using a combination of WL, X-ray and SZ data and set an upper limit of 11% on the level of non-thermal pressure at r 500 . The300 A1644 A1795 A2029 A2142 A2255 A2319 A3158 A3266 A644 A85 RXC1825 ZW1215 Fig.…”

“…The works cited above as well as those discussed in Section 2.4.1 have shown that stacked and radially-averaged SZ measurements in the cluster outskirts can be used in combination with lensing or X-ray constraints to infer the level of non-thermal pressure support (Siegel et al 2018), the level to which thermal HSE is valid , and may tentatively indicate a detection of the long-sought accretion shock (Hurier et al 2017). As SZ instrumentation progresses (see Section 4.1.4), SZ measurements will improve constraints on ICM turbulence and non-thermal pressure, allow tests of the nature of clumping in the outskirts, and provide better constraints on the accretion shock jump conditions.…”

Astrophysics with the Spatially and Spectrally Resolved Sunyaev-Zeldovich Effects

“…More recently, this effort has been extended using either the archival Planck or Bolocam datasets. These analyses include 1) a joint analysis of Bolocam and Chandra data for a large sample of 45 clusters probing the thermodynamics out to r 500 (Shitanishi et al 2018), 2) a detailed analysis using a sub-sample of 6 clusters including data from Bolocam, Chandra, the Hubble Space Telescope (HST), and Hyper Suprime-Cam (HSC) lensing data (Siegel et al 2018) to probe non-thermal pressure support out to r 500 , and 3) a large effort by the XMM Cluster Outskirts Project (XCOP) to study the thermodynamics, non-thermal pressure support, and outskirts (> r 500 ) of the 13 of the most significant Planck detections (see Eckert et al 2017aEckert et al , 2019Ettori et al 2019;Ghirardini et al 2019).…”

JoXSZ: Joint X-SZ fitting code for galaxy clusters

“…Several authors have paved the way for joint X-SZ analyses, mostly considering electron density and SZ data (e.g. Kitayama et al 2004;Ameglio et al 2007;Mroczkowski et al 2009;Eckert et al 2013;Adam et al 2015;Shitanishi et al 2018;Siegel et al 2018;Ghirardini et al 2018;Ruppin et al 2020), where electron density and SZ strength were often derived for a given temperature and metallicity. In a different approach, we replace the electron density fit with a full spatial-spectral Xray data fit.…”