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ABSTRACT We present detailed 16-GHz interferometric observations using the Arcminute Microkelvin Imager (AMI) of 19 clusters with LX > 7 × 1037 W ( h50 = 1) selected from the Local Cluster Substructure Survey (LoCuSS; 0.142 ≤ z ≤ 0.295) and of Abell 1758b, which is in the field of view of Abell 1758a. We detect and resolve Sunyaev-Zel'dovich (SZ) signals towards 17 clusters, with peak surface brightnesses between 5σ and 23σ. We use a fast, Bayesian cluster analysis to obtain cluster parameter estimates in the presence of radio point sources, receiver noise and primordial cosmic microwave background (CMB) anisotropy. We fit isothermal β-models to our data and assume the clusters are virialized (with all the kinetic energy in gas internal energy). Our gas temperature, TAMI, is derived from AMI SZ data and not from X-ray spectroscopy. Cluster parameters internal to r500 are derived under the assumption of hydrostatic equilibrium. We find the following. (i) Different generalized Navarro-Frenk-White (gNFW) parametrizations yield significantly different parameter degeneracies. (ii) For h70 = 1, we find the classical virial radius, r200, to be typically 1.6 ± 0.1 Mpc and the total mass MT( r200) typically to be 2.0-2.5× MT( r500). (iii) Where we have found MT( r500) and MT( r200) X-ray and weak-lensing values in the literature, there is good agreement between weak-lensing and AMI estimates (with [ABSTRACT FROM AUTHOR]

ABSTRACT We present observations using the Small Array of the Arcminute Microkelvin Imager (AMI; 14-18 GHz) of four Abell and three MACS clusters spanning 0.171-0.686 in redshift. We detect Sunyaev-Zel'dovich (SZ) signals in five of these without any attempt at source subtraction, although strong source contamination is present. With radio-source measurements from high-resolution observations, and under the assumptions of spherical β-model, isothermality and hydrostatic equilibrium, a Bayesian analysis of the data in the visibility plane detects extended SZ decrements in all seven clusters over and above receiver noise, radio sources and primary cosmic microwave background imprints. Formal Bayesian evidence ratios range from 1011:1 to 1043:1 for six of the clusters and 3000:1 for one with substantially fewer data than the others. We present posterior probability distributions for, e.g., total mass and gas fraction averaged over radii internal to which the mean overdensity is 1000, 500 and 200, r200 being the virial radius. Reaching r200 involves some extrapolation for the nearer clusters but not for the more distant ones. We find that our estimates of gas fraction are low (compared with most in the literature) and decrease with increasing radius. These results appear to be consistent with the notion that gas temperature in fact falls with distance (away from near the cluster centre) out to the virial radius. [ABSTRACT FROM AUTHOR]

BSTRACT [ABSTRACT FROM AUTHOR]

Using the Arcminute Microkelvin Imager (AMI) at 16 GHz and the Very Small Array (VSA) at 33 GHz to make follow-up observations of sources in the New Extragalactic WMAP Point Source catalogue, we have investigated the flux density variability in a complete sample of 97 sources over time-scales of a few months to ≈1.5 yr. We find that 53 per cent of the 93 sources, for which we have multiple observations, are variable, at the 99 per cent confidence level, above the flux density calibration uncertainties of ≈4 per cent at 16 GHz; the fraction of sources having varied by more than 20 per cent is 15 per cent at 16 GHz and 20 per cent at 33 GHz. Not only is this common occurrence of variability at high frequency of interest for source physics, but also strategies for coping with source contamination in cosmic microwave background work must take this variability into account. There is no strong evidence of a correlation between variability and flux density for the sample as a whole. For those sources classified as variable, the mean fractional rms variation in flux density increases significantly with the length of time separating observation pairs. Using a maximum likelihood method, we calculate the correlation in the variability at the two frequencies in a subset of sources classified as variable from both the AMI and VSA data and find the Pearson product–moment correlation coefficient to be very high . We also find the degree of variability at 16 GHz to be very similar to that at 33 GHz . Finally, we have investigated the relationship between variability and spectral index, (where ), and find a significant difference in the spectral indices of the variable sources and non-variable sources . [ABSTRACT FROM AUTHOR]