Statistics
Scientific paper
Dec 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aas...19916116e&link_type=abstract
American Astronomical Society, 199th AAS Meeting, #161.16; Bulletin of the American Astronomical Society, Vol. 34, p.574
Statistics
Scientific paper
Using million object samples extracted from billion particle dark matter clustering simulations, we study various characteristics of the cosmic population of galaxy clusters and superclusters. Samples are drawn from final configurations of Hubble Volume regions along with mock sky surveys of dark matter structure extending to redshifts z ~ 1.4 over 10,000 sq deg and to z ~ 0.5 over two full spheres. The size and scale of the data set allow a number of unique calibration opportunities. From cosmic variance in the number density of local cluster samples, we show that the mean squared error in power spectrum normalization σ 8 derived from local X-ray data is roughly doubled. This larger normalization error increases the uncertainty in predictions for the number of clusters expected to lie at high redshift. We point out some statistics -- namely, the mean redshift of mass limited sky survey samples and the redshift behavior of the characteristic temperature at fixed sky surface density -- that are relatively insensitive to normalization error. A Λ CDM (Ω m=0.3, Ω Λ =0.7) universe with σ 8 =0.9 harbors an abundant population of high redshift clusters. In the Sloan Survey area, one 8 keV cluster should lie at z>2 and ten 5 keV clusters at z>3. Too many such clusters can falsify the model; detection of Coma-sized clusters at z>1 violates Λ CDM at 95% confidence if their surface density exceeds 0.003 per sq deg, or 120 on the whole sky. Ongoing analysis of cluster scaling relations, superclusters and internal structure will also be presented, including research by UM undergraduates investigating: 1) statistical distributions of cluster shapes (to low order in density and velocity), and their spatial correlations; 2) internal radial dark matter profiles derived from stacked cluster samples.
Dykema P. M.
Evrard August E.
Kasun S. F.
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