Lorenzo IngogliaCOVONE, GIOVANNIGIOVANNICOVONESereno, MauroMauroSerenoGIOCOLI, CarloCarloGIOCOLIBARDELLI, SandroSandroBARDELLIFabio BellagambaGianluca CastignaniSamuel FarrensHendrik HildebrandtShahab JoudakiEric JulloDenise LanzieriGiorgio F. LesciFederico MarulliMatteo MaturiMOSCARDINI, LAUROLAUROMOSCARDINILorenza NanniPUDDU, Emanuella AnnaEmanuella AnnaPUDDURADOVICH, MARIOMARIORADOVICHRONCARELLI, MauroMauroRONCARELLISAPIO, FELICIANAFELICIANASAPIOCarlo Schimd2023-05-222023-05-2220220035-8711http://hdl.handle.net/20.500.12386/34186Galaxy clusters are biased tracers of the underlying matter density field. At very large radii beyond about 10 Mpc/\textit{h}, the shear profile shows evidence of a second-halo term. This is related to the correlated matter distribution around galaxy clusters and proportional to the so-called halo bias. We present an observational analysis of the halo bias-mass relation based on the AMICO galaxy cluster catalog, comprising around 7000 candidates detected in the third release of the KiDS survey. We split the cluster sample into 14 redshift-richness bins and derive the halo bias and the virial mass in each bin by means of a stacked weak lensing analysis. The observed halo bias-mass relation and the theoretical predictions based on the $\Lambda$CDM standard cosmological model show an agreement within $2\sigma$. The mean measurements of bias and mass over the full catalog give $M_{200c} = (4.9 \pm 0.3) \times 10^{13} M_{\odot}/\textit{h}$ and $b_h \sigma_8^2 = 1.2 \pm 0.1$. With the additional prior of a bias-mass relation from numerical simulations, we constrain the normalization of the power spectrum with a fixed matter density $\Omega_m = 0.3$, finding $\sigma_8 = 0.63 \pm 0.10$.STAMPAenAMICO galaxy clusters in KiDS-DR3: Measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysisArticle10.1093/mnras/stac046https://academic.oup.com/mnras/article/511/1/1484/65051572022MNRAS.511.1484IFIS/05 - ASTRONOMIA E ASTROFISICAERC sectors::Physical Sciences and Engineering