Figure 1: Generated cluster distribution as a function of redshift and integrated Compton flux. | |
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Figure 2: Simulations of sky maps, as observed by a large-array bolometer experiment. For these simulations, we used the Olimpo experiment model. From left to right, 143, 217, 385, and 600 GHz bands are shown. In the two lower frequencies band, CMB primordial anisotropies are the dominant features. At higher frequencies bands, bright Infrared galaxies and Galactic dust become dominant. The SZ cluster signal is sub-dominant at all frequencies. | |
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Figure 3: Left: cluster reconstructed flux versus the true simulated flux, and our photometry model contours. Dashed lines are the one sigma error and dash-dotted are the 2 sigmas errors; the continuous line is the mean. 20 cumulative Monte-Carlo simulations where used for this plot. Right: SZ cluster reconstructed virial size versus true simulated virial size. Although the normalization is not correct, a small correlation is visible for large clusters. | |
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Figure 4: Completness as a function of redshift for flux (left) and mass (middle), as simulated from a semi-analytic large scale structure and cosmology model. We used design parameters of the Olimpo project to model observation performance. Right: modelled selection function after extended simulations. | |
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Figure 5: We ran 100 montecarlo on 400 deg^{2}. Left: the black curve is the histogram of generated cluster flux, compared to the blue (dashed line) histogram of true cluster detection. Right: the blue (dashed line) histogram is the true cluster observed flux. The flux distribution of the contamination is plotted in (light line) orange. The (thin line) red curve is our modelled flux distribution of contamination. | |
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Figure 6: From left to right, first line: true clusters, contamination and sources counts histograms for 100 simulations. Red curve fits are used in the following observations' model. Second line show same results when assuming a perfect calibration of SZ cluster observations. | |
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Figure 7: Left: the cluster distribution generated by simulations, , and the observed cluster distribution, , for 100 averaged Monte-Carlo simulations (middle), and from the observation model (right). The axes are the integrated flux Y in versus redshift. | |
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Figure 8: Probability density of the recovered cosmological parameters and , for 100 full Monte-Carlo simulations and using the observation model in the Extented Likelyhood computations. Diamond is the model used at the input of simulations. Cross, is the maximun of occurrence of reconstructed parameters. | |
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Figure 9: Left: histogram of log-likelihood L (black) for N Monte-Carlo catalogue of a Press-Schechter cosmological model. The peak is fitted by a Gaussian law (red line), with mean . Right: red line is the probability versus of observing a Press-Schechter based catalogue with . Vertical dashed line is the computed for a catalogue generated from a Sheth and Tormen model. The probability of compatibility is lower than 10^{-5}. | |
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Figure 10: Left: histogram (red) of difference of log-likelihood for N=1000 Monte-Carlo catalogues of the best cosmological model according to our data set. Black line is the law, expected for Gaussian distributions with 2 degrees of freedom ( and ). 68%, 95% and 99% confidence levels are shown as dotted, dashed and dot-dashed horizontal blue lines. The approximation is very optimistic. | |
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Figure 11: Expected constraints on and from an Olimpo scientific flight, with full spectroscopic follow-up of the sources and a field to field Poisson-like count variance. All other cosmological parameters, have been set to the values in Table 1. Diamond is the initial cosmological model used to simulate data. | |
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Figure 12: Degradations of constraint due to the cluster count variance. White marks give the cosmological models used to simulate the data. Left: black lines are the vs. Confidence Level (1, 2 and 3 sigmas) contours computed using the degraded count variance, all other cosmological parameters set to their simulated values. Only the information on SZ-cluster count has been used in this figure, no redshift. Colored are the same CL constraints, with the Poisson-like field to field cluster count variation. Right: Confidence Level (CL) contours assuming 100% follow-up for cluster redshifts, and a degraded count variance as quantified at paragraph 2.5. All other cosmological parameters set to simulated values. Lines draw the CL contours with degraded field to field cluster count variation (black cross is the reconstructed model). Colored contours are the reference constraints as in Fig. 11. | |
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Figure 13: Left: confidence level map, on and T_{*}, marginalised on . Colors are computed from SZ-cluster data only, dashed lines uses WMAP and CFHTLS weak-shear Fisher matrix constraints, no systematic effect on WMAP or CFHT-LS are included. Right: lines are the constraints on cosmological parameters if we keep only the largest flux clusters. All other cosmological parameters, have been set. Diamond is the generated concordance model. Black cross is the reconstructed model. Colors delimit the references CL contour. Lower statistic induce heavy loss in the constraint accuracy. | |
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Figure 14: Left: impact on cosmological constraints, due to an incomplete redshift follow-up of cluster candidates. Black line is the 95% CL contour assuming a 10% coverage follow-up. Dashed line assumes 20% coverage follow-up and dotted line 50% coverage. Colored contours are a copy of Fig. 11. Right: lines show the systematic shift in the CL contour induced by neglecting contaminants in the recovered source catalogue. This plot was generated assuming that 50% of the sources have been observed in follow-up for redshift. Colors stand for contours computed with the same dataset, but taking into account contaminations. White cross is the best model taking into account contaminants, and black cross is the biased best model. The diamond is still the simulated cosmological model. | |
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