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Infrared-bright galaxies in the millennium simulation and Sunyaev Zeldovich effect contaminationOpolot, Daniel Christopher January 2010 (has links)
>Magister Scientiae - MSc / Measuring the evolution of the abundance of galaxy clusters puts constraints on cosmological parameters like the cosmological density parameter m, σ8 and the dark energy equation of state parameter, w. Current observations that promise to give large cluster counts and their properties are those that rely on the Sunyaev-Zeldovich effect (SZE) from clusters. We study the contamination of the SZ signals from galaxy clusters by cluster infrared (IR) galaxies and particularly faint IR galaxies. We use the Millennium simulation database to extract galaxy clusters and deduce contaminant IR fluxes using the star formation rate - IR luminosity relations. We use the IR spectral energy distribution(SED) to obtain the monochromatic fluxes at 145 GHz, 217 GHz and 265 GHz, which are the observation frequencies of the Atacama Cosmology Telescope (ACT). Taking ACT as a case study, we selected all clusters with Mvir ≥ 2 × 1014 M⊙, and consider all galaxies in a cluster with star formation rate sfr ≥ 0.2 M⊙yr−1 as IR galaxies. From the fluxes of these selected sources, we compute their contribution to the SZE temperature fluctuations.We find that the galaxies in clusters have a non-neglible contribution to the SZ signals.In massive and rich clusters the contribution can be as high as 100 μK at z = 0.36,which is substantial when compared to the thermal SZE of & 270μK for such clusters.This effect can be reduced significantly if proper modelling of IR sources is done to pick out the point sources within clusters. We also find that irrespective of the mass range,the average contaminant temperature fluctuation T can be modelled as a power-law: T = Czm, where z is the redshift, m = 1.8 ± 0.07 and C takes on a range of values(0.008 to 0.9) depending on the cluster mass and the observation frequency respectively.We also study some properties of simulated galaxy clusters like substructures in clusters,2D projected distributions and number density profiles, which are all discussed in the
results.
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