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|Title:||Comparing approximate methods for mock catalogues and covariance matrices - I. Correlation function||Authors:||Lippich, Martha
Sánchez, Ariel G.
Alvarez, Marcelo A.
Dalla Vecchia, Claudio
|Issue Date:||2019||Journal:||MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY||Number:||482||Issue:||2||First Page:||1786||Abstract:||This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation function measurements. Our comparison includes seven approximate methods, which can be divided into three categories: predictive methods that follow the evolution of the linear density field deterministically (ICE-COLA, PEAK PATCH, and PINOCCHIO), methods that require a calibration with N-body simulations (PATCHY and HALOGEN), and simpler recipes based on assumptions regarding the shape of the probability distribution function (PDF) of density fluctuations (lognormal and Gaussian density fields). We analyse the impact of using covariance estimates obtained from these approximate methods on cosmological analyses of galaxy clustering measurements, using as a reference the covariances inferred from a set of full N-body simulations. We find that all approximate methods can accurately recover the mean parameter values inferred using the N-body covariances. The obtained parameter uncertainties typically agree with the corresponding N-body results within 5 per cent for our lower mass threshold and 10 per cent for our higher mass threshold. Furthermore, we find that the constraints for some methods can differ by up to 20 per cent depending on whether the halo samples used to define the covariance matrices are defined by matching the mass, number density, or clustering amplitude of the parent N-body samples. The results of our configuration-space analysis indicate that most approximate methods provide similar results, with no single method clearly outperforming the others.||URI:||http://hdl.handle.net/20.500.12386/30928||URL:||https://academic.oup.com/mnras/article/482/2/1786/5136430||ISSN:||0035-8711||DOI:||10.1093/mnras/sty2757||Bibcode ADS:||2019MNRAS.482.1786L||Fulltext:||open|
|Appears in Collections:||1.01 Articoli in rivista|
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checked on Jun 25, 2021
checked on Jun 25, 2021
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