The persistent cosmic web and its filamentary structure II: Illustrations

TL;DR

This paper demonstrates the application of the DisPerSE algorithm to cosmological data, effectively identifying filaments, walls, and voids in simulations and observations, and introduces a topologically motivated significance criterion for structure detection.

Contribution

It extends the DisPerSE method to real data, providing a self-consistent significance measure for cosmic structures and validating its effectiveness on SDSS and X-ray observations.

Findings

DisPerSE traces cosmic filaments, walls, and voids accurately in simulations and SDSS data.

The significance criterion based on persistence ratio effectively distinguishes real structures from noise.

The method recovers topological invariants directly from particle data, even with sparse sampling.

Abstract

The recently introduced discrete persistent structure extractor (DisPerSE, Soubie 2010, paper I) is implemented on realistic 3D cosmological simulations and observed redshift catalogues (SDSS); it is found that DisPerSE traces equally well the observed filaments, walls, and voids in both cases. In either setting, filaments are shown to connect onto halos, outskirt walls, which circumvent voids. Indeed this algorithm operates directly on the particles without assuming anything about the distribution, and yields a natural (topologically motivated) self-consistent criterion for selecting the significance level of the identified structures. It is shown that this extraction is possible even for very sparsely sampled point processes, as a function of the persistence ratio. Hence astrophysicists should be in a position to trace and measure precisely the filaments, walls and voids from such samples and assess the confidence of the post-processed sets as a function of this threshold, which can be expressed relative to the expected amplitude of shot noise. In a cosmic framework, this criterion is comparable to friend of friend for the identifications of peaks, while it also identifies the connected filaments and walls, and quantitatively recovers the full set of topological invariants (Betti numbers) {\sl directly from the particles} as a function of the persistence threshold. This criterion is found to be sufficient even if one particle out of two is noise, when the persistence ratio is set to 3-sigma or more. The algorithm is also implemented on the SDSS catalogue and used to locat interesting configurations of the filamentary structure. In this context we carried the identification of an ``optically faint'' cluster at the intersection of filaments through the recent observation of its X-ray counterpart by SUZAKU. The corresponding filament catalogue will be made available online.