The Square Kilometre Array (SKA)

Abstract:

We summarise the properties of the absorbing haloes around two high-redshift radio galaxies studied with the VLT-UVES echelle spectrograph. The absorbers in 0943 - 242 (z = 2.92) exhibit little further structure compared with previous data-the main absorber still has N(HI) similar or equal to 10(19) cm(-2)-reinforcing the picture in which the absorbing gas occupies a smooth shell beyond the emission line halo. In 0200+015 (z = 2.23) the main absorber now splits into two spatially-resolved systems with N(HI) = 4 x 10(14) cm(-2), one of which has strong CIV absorption, implying a metallicity of Z similar or equal to 10 Zcircle dot. We explain the differences between the two sources with an evolutionary scenario, invoking interaction with the expanding radio source and metal enrichment via a starburst superwind. (C) 2003 Elsevier B.V. All rights reserved.

Abstract:

We have tested Very Small Array (VSA) observations of three regions of sky for the presence of non-Gaussianity, using high-order cumulants, Minkowski functionals, a wavelet-based test and a Bayesian joint power spectrum/non-Gaussianity analysis. We find the data from two regions to be consistent with Gaussianity. In the third region, we obtain a 96.7% detection of non-Gaussianity using the wavelet test. We perform simulations to characterise the tests, and conclude that this is consistent with expected residual point source contamination. There is therefore no evidence that this detection is of cosmological origin. Our simulations show that the tests would be sensitive to any residual point sources above the data's source subtraction level of 20 mJy. The tests are also sensitive to cosmic string networks at an rms fluctuation level of $105 μK$ (i.e. equivalent to the best-fit observed value). They are not sensitive to string-induced fluctuations if an equal rms of Gaussian CDM fluctuations is added, thereby reducing the fluctuations due to the strings network to $74 μK$ rms . We especially highlight the usefulness of non-Gaussianity testing in eliminating systematic effects from our data.