The clustering of critical points in the evolving cosmic web

Abstract

Focusing on both small separations and Baryonic Acoustic Oscillation scales, the cosmic evolution of the clustering properties of peak, void, wall and filament-type critical points is measured using two-point correlation functions in ΛCDM dark matter simulations as a function of their relative rarity. A qualitative comparison to the corresponding theory for Gaussian Random fields allows us to understand the following observed features: i) the appearance of an exclusion zone at small separation, whose size depends both on rarity and on the signature (i.e. the number of negative eigenvalues) of the critical points involved; ii) the amplification of the Baryonic Acoustic Oscillation bump with rarity and its reversal for cross correlations involving negatively biased critical points; iii) the orientation-dependent small-separation divergence of the cross-correlations of peaks and filaments (resp. voids and walls) which reflects the relative loci of such points in the filament's (resp. wall's) eigenframe. The most significant features of the correlations are tabulated. The (cross-) correlations involving the most non-linear critical points (peaks, voids) display significant variation with redshift, while those involving less non-linear critical points seem mostly insensitive to redshift evolution, which should prove advantageous to model. The relative distances to the maxima of the peak-to-wall and peak-to-void over that of the peak-to-filament cross-correlation are in ratios of ∼ √ 2 and ∼ √ 3, respectively which could be interpreted as an indication of the cosmic crystal being on average close to a cubic lattice. The insensitivity to redshift evolution suggests that the absolute and relative clustering of critical points could become a topologically robust alternative to standard clustering techniques when analysing upcoming large scale surveys such as Euclid or LSST.

Gas distribution from clusters to filaments in IllustrisTNG

“…Under the action of gravity, large-scale cosmic flows transport matter on large-scale cosmic structures from voids to sheets, and then via elongated filaments into clusters (Zel'Dovich 1970). The anisotropic large-scale matter distribution and its associated accretion processes have been investigated both theoretically and via N-body simulations (Pichon et al 2010;Codis et al 2015;Shim et al 2020) to establish a picture of the evolutionary and dynamical aspects of the cosmic network (see e.g. Hahn 2016 for a review).…”

Gas distribution from clusters to filaments in IllustrisTNG

“…Under the action of gravity, large-scale cosmic flows transport matter on cosmic large-scale structures from voids to sheets, and then via elongated filaments into clusters (Zel'Dovich 1970). The anisotropic large-scale matter distribution and its associated accretion processes have been investigated both theoretically and via N-body simulations (Pichon et al 2010;Codis et al 2015;Shim et al 2020), to establish a picture on the evolutionary and dynamical aspects of the cosmic network (see e.g. Hahn 2016, for a review).…”

Shape and connectivity of groups and clusters: Impact of dynamical state and accretion history

“…The statistics of halo connectivity is expected to depend on the growth factor, and thus can constitute an new topological constraint on dark energy (Codis et al 2018), as the connectivity of halos must decrease with the cosmic time due to the Universe accelerated expansion which disconnects cosmic nodes from the network of filaments (Pichon et al 2010). More generally, the anisotropic environment of the cosmic web impacts the assembly bias of halos as theoretically explored by Musso et al (2018) who found that early-formed low-mass halos lie preferentially along filaments, while massive young ones are closer to the nodes (see also Shim et al 2020;Cadiou et al 2020). Cosmic web anisotropy is thus expected to be an indicator of halo assembly bias, and is therefore strongly correlated with halo properties (as discussed in Paranjape et al 2018;Ramakrishnan et al 2019).…”