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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 deg2. 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,
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Figure 8:
Probability density of the recovered cosmological 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
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Figure 10:
Left: histogram (red) of difference of log-likelihood ![]() ![]() ![]() ![]() ![]() ![]() |
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Figure 11:
Expected constraints on ![]() ![]() |
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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
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Figure 13:
Left: confidence level map, on ![]() ![]() |
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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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