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Euclid: Effect of sample covariance on the number counts of galaxy clusters
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Euclid: Effect of sample covariance on the number counts of galaxy clusters
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Aims. We investigate the contribution of shot-noise and sample variance to the uncertainty of cosmological parameter constraints inferred from cluster number counts in the context of the Euclid survey. Methods. By analysing 1000 Euclid-like light-cones, produced with the PINOCCHIO approximate method, we validate the analytical model of Hu & Kravtsov 2003 for the covariance matrix, which takes into account both sources of statistical error. Then, we use such covariance to define the likelihood function that better extracts cosmological information from cluster number counts at the level of precision that will be reached by the future Euclid photometric catalogs of galaxy clusters. We also study the impact of the cosmology dependence of the covariance matrix on the parameter constraints. Results. The analytical covariance matrix reproduces the variance measured from simulations within the 10 per cent level; such difference has no sizeable effect on the error of cosmological parameter constraints at this level of statistics. Also, we find that the Gaussian likelihood with cosmology-dependent covariance is the only model that provides an unbiased inference of cosmological parameters without underestimating the errors.
Forward citations
Cited by 2 Pith papers
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Accelerating cosmological simulations on GPUs: a portable approach using OpenMP
An OpenMP-based GPU port of PINOCCHIO's collapse-time kernel reaches 4x (NVIDIA) to 8x (AMD) speedups and matches CPU interpolation results to ~0.003%.
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