Abstract

ABSTRACT Upcoming wide-field surveys are well suited to studying the growth of galaxy clusters by tracing galaxy and gas accretion along cosmic filaments. We use hydrodynamic simulations of volumes surrounding 324 clusters from The ThreeHundred project to develop a framework for identifying and characterizing these filamentary structures and associating galaxies with them. We define three-dimensional reference filament networks reaching 5R200 based on the underlying gas distribution and quantify their recovery using mock galaxy samples mimicking observations such as those of the WEAVE Wide-Field Cluster Survey. Since massive galaxies trace filaments, they are best recovered by mass-weighting galaxies or imposing a bright limit (e.g. >L*) on their selection. We measure the transverse gas density profile of filaments, derive a characteristic filament radius of ≃ 0.7–1 h−1Mpc, and use this to assign galaxies to filaments. For different filament extraction methods, we find that at R > R200, ∼15–$20{{\ \rm per\ cent}}$ of galaxies with M* > 3 × 109M⊙ are in filaments, increasing to $\sim 60{{\ \rm per\ cent}}$ for galaxies more massive than the Milky Way. The fraction of galaxies in filaments is independent of cluster mass and dynamical state and is a function of cluster-centric distance, increasing from ∼13 per cent at 5R200 to ∼21 per cent at 1.5R200. As a bridge to the design of observational studies, we measure the purity and completeness of different filament galaxy selection strategies. Encouragingly, the overall three-dimensional filament networks and ∼67 per cent of the galaxies associated with them are recovered from two-dimensional galaxy positions.

Highlights

  • The matter distribution of the Universe follows a web-like structure, consisting of sheets, filaments, knots and voids, providing the environment in which galaxies form and evolve (Bond et al 1996)

  • This value was chosen to ensure that cluster centres and massive groups are detected as nodes, and filaments connected to the main halo terminate in saddle points2 Subsequently, we cleaned and simplified the DisPerSE outputs for our purposes by matching the ends of segments and tracing the matches from each saddle point

  • Filaments are regarded as a crucial pathway for transporting matter into galaxy clusters

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Summary

INTRODUCTION

The matter distribution of the Universe follows a web-like structure, consisting of sheets, filaments, knots and voids, providing the environment in which galaxies form and evolve (Bond et al 1996). The outskirts of galaxy clusters are the points of contact that link the large scale cosmic web to the confined realms of cluster cores at their knots They have emerged as one of the new frontiers and unique laboratories to study the mass assembly in the Universe as well as galaxy evolution in the context of global environment (Walker et al 2019). Though the controversy is not solved, this suggests that the multi-stream region of the large scale structure does have a secondary effect (besides the local environment) and that galaxies accreted by clusters become affected well before they reach the cluster centre In response to these challenges, future surveys will explore the filamentary structures far beyond the virial radius of clusters as important sites of galaxy evolution.

The ThreeHundred cluster project
Data products: halo catalogues
Dynamical relaxation of clusters
Filament finding
Cosmic filament reconstruction with DisPerSE
Filament extraction using smoothed gas particles
Stability of filament networks over time
Filaments align with the shape of the central halo
Thickness of filaments
TOWARDS OBSERVATIONS
Mass-weighted mock galaxies filament extraction
Comparison of 3D gas filaments to 3D mock galaxy filaments
Comparison of filaments extracted from mock galaxies in 3D and projected 2D
The impact of filament extractions
The impact of detection limits and cluster properties
A pile-up of galaxies in filaments closer to cluster centres
Performance evaluation for observations
The impact of filament-detection methods on recovery rates
The impact of projections on recovery rates
Findings
CONCLUSIONS
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