Detection and analysis of cluster–cluster filaments

Abstract

In this work, we identify and analyse the properties of cluster-cluster filaments within a cosmological simulation assuming that they are structures connecting maxima of the density field defined by dark matter halos with masses M ≥ 1014 h−1M⊙. To extract these filaments we develop an identification algorithm based on two standard tools: the Minimal Spanning Tree (MST) and the Friends of Friends (FoF) algorithm. Focusing our analysis on the densest dark matter filaments, we found that the radial density profile, at scales around 1 h−1Mpc, approximately follow a power-law function with index -2. Without making any assumption about the velocity field, our algorithm finds that the saddle point arises as a natural characteristic of the filamentary structure. In addition, its location along the filament depends on the masses of the halos at the filament ends. We also found that the infall velocities follow a cross-pattern near the saddle point, being perpendicular to the filament spine when approaching from low-density regions, and parallel away from the saddle point towards the ends of the filament. Following theoretical prescriptions, we estimate the linear density from the transverse velocity dispersion, finding a good correspondence with the measured mass per unit length of our filaments. Our results can be applied to observational samples of filaments in order to link the saddle point location and the mass per unit length with measurements obtained from observations such as cluster masses and the velocity dispersion of galaxies.

Infall of galaxies onto groups

“…The V infall /V infall ⊥ ratio for the two group samples remains below 1 (solid line, V infall ⊥ > V infall ), indicating that the streaming motion of galaxies onto groups with large underdense regions is faster than those of galaxies from global overdensities. This result is in sync with those of Pereyra et al (2019), Mahajan et al (2012) and Ceccarelli et al (2011), which showed that a relatively high galaxy density in the infalling regions of groups promotes tidal interactions with neighboring galaxies, resulting in a smaller magnitude of the velocities along these high-density regions. We stress the fact that both subsamples of groups have similar mass and luminosity distributions, so these differences should be put down to an environmental difference.…”

Gas distribution from clusters to filaments in IllustrisTNG

“…Given the large diversity of filament types (in term of length and width, see for example Cautun et al 2014;Galárraga-Espinosa et al 2020), different approaches have been developed to detect them, and lead to their own filament definition. One can cite topology-based (Sousbie 2011;Aragón-Calvo et al 2010a), hessian-based (Hahn et al 2007;Cautun et al 2013) E-mail: celinegouin@kias.re.kr and geometry-based (Tempel et al 2016;Pereyra et al 2020;Bonnaire et al 2020Bonnaire et al , 2021 filament-finder techniques. These cosmic web classification and detection methods are crucial to explore how cosmic web environment drives the physical properties of it content: gas, galaxies, and DM.…”

Properties of gas phases around cosmic filaments atz= 0 in the IllustrisTNG simulation

“…Several algorithms, which detect filaments using various approaches, have been developed to overcome these limitations. For example, filaments can be defined based on the topology of the matter density field, on density thresholds, on the velocity field, or even with a probabilistic approach (see for example Aragón-Calvo et al 2010;Sousbie 2011;Sousbie et al 2011;Cautun et al 2013;Libeskind et al 2018;Bonnaire et al 2020;Pereyra et al 2020).…”