Constraining the cosmological parameters through the galaxy cluster mass function in the AMICO KiDS-DR3 catalogue

In this Thesis, we present a new cosmological analysis of cluster counts, exploiting the AMICO KiDS-DR3 catalogue (Maturi et al. 2019), which is asample of photometrically-selected galaxy clusters, built up through the use of the cluster detection algorithm called Adaptive Matched Identifier of Clustered Objects (AMICO) (Bellagamba et al. 2018), on the third data release of the Kilo Degree Survey (KiDS; de Jong et al. 2017). The catalogue contains 7988 candidate galaxy clusters, in the redshift range z∈[0.1,0.8], on an effective area of 377 deg2. We develop a method to assign a statistical weight to each object in the catalogue, by deriving the selection function of the sample from a mock catalogue. Exploiting the results of the weak-lensing analysis provided by Bellagamba et al. (2019), we derive the masses of the objects. Then we develop two cosmological pipelines in order to retrieve in a ΛCDM Universe the constraints on the matter density parameter, Ωm, the normalization of the power spectrum, σ8, and the cluster normalization condition parameter, S8=σ8(Ωm/0.3)0.5, fixing the other parameters at the values obtained by Planck Collab. (2018). In order to accomplish this task, we introduce a new model for the cluster counts. Subsequently, we implement in the analysis a peculiar source of uncertainty, namely the super sample covariance, aiming at accounting for systematics that intrinsically affects our data. We derive the following constraints: Ωm= 0.28+0.01−0.01, σ8= 0.85+0.02−0.01, S8= 0.82+0.01−0.01, which are consistent with ΛCDM predictions, and competitive, in terms of uncertainties, with results of state-of-the-art cluster number count analyses.