The Three Hundred : contrasting clusters galaxy density in hydrodynamical and dark matter simulations

Abstract

Cluster number counts will be a key cosmological probe in the next decade thanks to the Euclid satellite mission. For this purpose, cluster detection algorithm performance, which are sensitive to the spatial distribution of the cluster galaxy members and their luminosity function, need to be accurately characterized. Using The Three Hundred hydrodynamical and dark matter only simulations we study a complete sample of massive clusters beyond 7 (5) $\times$ 10${}^{14}$ M${}_{\odot }$ at redshift 0 (1) on a $(1.48\mathrm{Gpc}{)}^3$ volume. We find that the mass resolution of the current hydrodynamical simulations (1.5 $\times$ 10${}^9$ M${}_{\odot }$) is not enough to characterize the luminosity function of the sample in the perspective of Euclid data. Nevertheless, these simulations are still useful to characterize the spatial distribution of the cluster substructures assuming a common relative mass threshold for the different flavours and resolutions. By comparing with the dark matter only version of these simulations, we demonstrate that baryonic physics preserves significantly low mass subhalos (galaxies) as have also been observed in previous studies with less statistics. Furthermore, by comparing the hydro simulations with higher resolution dark matter only simulations of the same objects and taking the same limit in subhalo mass we find significantly more cuspy galaxy density profiles towards the center of the clusters, where the low mass substructures would tend to concentrate. We conclude that using dark matter only simulation may lead to some biases on the spatial distribution and density of galaxy cluster members. Based on the preliminary analysis of few high resolution hydro simulations we conclude that a mass resolution of 1.8 $\times$ 10${}^8$ h${}^{-1}$ M${}_{\odot }$ will be needed for The Three Hundred simulations to approach the expected magnitude limits for the Euclid survey.

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Cited by in corpus (1)

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