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Both simulation and observational data have shown that the spin and shape of dark matter halos are correlated with their nearby large-scale environment. As structure formation on different scales is strongly coupled, it is trick to disentangle the formation of halo with the large-scale environment, making it difficult to infer which is the driving force for the correlation between halo spin/shape with the large-scale structure. In this paper, we use N-body simulation to produce twin Universes that share the same initial conditions on small scales but different on large scales. This is achieved by changing the random seeds for the phase of those k modes smaller than a given scale in the initial conditions. In this way, we are able to disentangle the formation of halo and large-scale structure, making it possible to investigate how halo spin and shape correspond to the change of environment on large scales. We identify matching halo pairs in the twin simulations as those sharing the maximum number of identical particles within each other. Using these matched halo pairs, we study the cross match of halo spin and their correlation with the large-scale structure. It is found that when the large-scale environment changes (eigenvector) between the twin simulations, the halo spin has to rotate accordingly, although not significantly, to maintain the universal correlation seen in each simulation. Our results suggest that the large-scale structure is the main factor to drive the correlation between halo properties and their environment.
We investigate the spin evolution of dark matter haloes and their dependence on the number of connected filaments from the cosmic web at high redshift (spin-filament relation hereafter). To this purpose, we have simulated $5000$ haloes in the mass ra
We explore the evolution of halo spins in the cosmic web using a very large sample of dark matter haloes in the $Lambda$CDM Planck-Millennium N-body simulation. We use the NEXUS+ multiscale formalism to identify the hierarchy of filaments and sheets
We introduce the NEXUS algorithm for the identification of Cosmic Web environments: clusters, filaments, walls and voids. This is a multiscale and automatic morphological analysis tool that identifies all the cosmic structures in a scale free way, wi
We present evidence for halo assembly bias as a function of geometric environment. By classifying GAMA galaxy groups as residing in voids, sheets, filaments or knots using a tidal tensor method, we find that low-mass haloes that reside in knots are o
Using a set of high-resolution simulations we study the statistical correlation of dark matter halo properties with the large-scale environment. We consider halo populations split into four Cosmic Web (CW) elements: voids, walls, filaments, and nodes