Browsing by Subject "GALAXY CLUSTER SURVEY"

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  • Ade, P. A. R.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kiiveri, K.; Kurki-Suonio, H.; Lähteenmäki, Anne; Lindholm, Valtteri; Poutanen, T.; Savelainen, M.; Suur-Uski, A-S.; Tuovinen, J.; Valiviita, J.; Planck Collaboration (2014)
  • Planck Collaboration; Aghanim, N.; Keihänen, E.; Kiiveri, K.; Kurki-Suonio, H.; Lindholm, V.; Savelainen, M.; Valiviita, J. (2018)
    Using the Planck full-mission data, we present a detection of the temperature (and therefore velocity) dispersion due to the kinetic Sunyaev-Zeldovich (kSZ) effect from clusters of galaxies. To suppress the primary CMB and instrumental noise we derive a matched filter and then convolve it with the Planck foreground-cleaned "2D- ILC" maps. By using the Meta Catalogue of X-ray detected Clusters of galaxies (MCXC), we determine the normalized rms dispersion of the temperature fluctuations at the positions of clusters, finding that this shows excess variance compared with the noise expectation. We then build an unbiased statistical estimator of the signal, determining that the normalized mean temperature dispersion of 1526 clusters is = (1.64 +/- 0.48) x 10(-11). However, comparison with analytic calculations and simulations suggest that around 0.7 sigma of this result is due to cluster lensing rather than the kSZ effect. By correcting this, the temperature dispersion is measured to be = (1.35 +/- 0.48) x 10(-11), which gives a detection at the 2.8 sigma level. We further convert uniform-weight temperature dispersion into a measurement of the line-of-sight velocity dispersion, by using estimates of the optical depth of each cluster (which introduces additional uncertainty into the estimate). We find that the velocity dispersion is (v(2)) = (123 000 +/- 71 000) (km s(-1))(2), which is consistent with findings from other large-scale structure studies, and provides direct evidence of statistical homogeneity on scales of 600 h(-1) Mpc. Our study shows the promise of using cross-correlations of the kSZ effect with large-scale structure in order to constrain the growth of structure.
  • Ade, P. A. R.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kurki-Suonio, H.; Poutanen, T.; Suur-Uski, A. -S.; Planck Collaboration (2014)
  • Clerc, N.; Ramos-Ceja, M. E.; Ridl, J.; Lamer, G.; Brunner, H.; Hofmann, F.; Comparat, J.; Pacaud, F.; Käfer, F.; Reiprich, T. H.; Merloni, A.; Schmid, C.; Brand, T.; Wilms, J.; Friedrich, P.; Finoguenov, A.; Dauser, T.; Kreykenbohm, I. (2018)
    Context. Studies of galaxy clusters provide stringent constraints on models of structure formation. Provided that selection effects are under control, large X-ray surveys are well suited to derive cosmological parameters, in particular those governing the dark energy equation of state. Aims. We forecast the capabilities of the all-sky eROSITA (extended ROentgen Survey with an Imaging Telescope Array) survey to be achieved by the early 2020s. We bring special attention to modelling the entire chain from photon emission to source detection and cataloguing. Methods. The selection function of galaxy clusters for the upcoming eROSITA mission is investigated by means of extensive and dedicated Monte-Carlo simulations. Employing a combination of accurate instrument characterisation and a state-of-the-art source detection technique, we determine a cluster detection efficiency based on the cluster fluxes and sizes. Results. Using this eROSITA cluster selection function, we find that eROSITA will detect a total of approximately 10(5) clusters in the extra-galactic sky. This number of clusters will allow eROSITA to put stringent constraints on cosmological models. We show that incomplete assumptions on selection effects, such as neglecting the distribution of cluster sizes, induce a bias in the derived value of cosmological parameters. Conclusions. Synthetic simulations of the eROSITA sky capture the essential characteristics impacting the next-generation galaxy cluster surveys and they highlight parameters requiring tight monitoring in order to avoid biases in cosmological analyses.