Characterising filaments in the SDSS volume from the galaxy distribution

Abstract

Detecting the large-scale structure of the Universe based on the galaxy distribution and characterising its components is of fundamental importance in astrophysics but is also a difficult task to achieve. Wide-area spectroscopic redshift surveys are required to accurately measure galaxy positions in space that also need to cover large areas of the sky. It is also difficult to create algorithms that can extract cosmic web structures (e.g. filaments). Moreover, these detections will be affected by systematic uncertainties that stem from the characteristics of the survey used (e.g. its completeness and coverage) and from the unique properties of the specific method adopted to detect the cosmic web (i.e. the assumptions it relies on and the free parameters it may employ). For these reasons, the creation of new catalogues of cosmic web features on wide sky areas is important, as this allows users to have at their disposal a well-understood sample of structures whose systematic uncertainties have been thoroughly investigated. In this paper we present the filament catalogues created using the discrete persistent structure extractor tool in the Sloan Digital Sky Survey (SDSS), and we fully characterise them in terms of their dependence on the choice of parameters pertaining to the algorithm, and with respect to several systematic issues that may arise in the skeleton as a result of the properties of the galaxy distribution (such as Finger-of-God redshift distortions and defects of the density field that are due to the boundaries of the survey).

the threehundred: the structure and properties of cosmic filaments in the outskirts of galaxy clusters

“…Filaments run from a node to a saddle point and at least two are needed to bridge between haloes. studies mentioned above, the trend of having a lower population for longer filaments seen here is widely observed in the literature (Bond, Strauss & Cen 2010;Galárraga-Espinosa et al 2020;Malavasi et al 2020;Rost et al 2020). Note also that the recovered length and number of filaments depend on the persistence level chosen during filament extraction, with a lower level leading to more nodes and a finer network.…”

“…Other studies such as Malavasi et al (2020), Tanimura et al (2020), Gheller et al (2015), and Gheller & Vazza (2019) focus on long filaments of dozens of Mpc, whereas others like Kraljic et al (2019) focus on filaments in the range 3-12Mpc. Here, we report the distance between a node and a saddle point, the length of filaments from one node to the other will typically be twice this value as normally both halves of a bridge between two nodes will be counted separately.…”

First detection of stacked X-ray emission from cosmic web filaments

“…Statistical detections of gas in stacked short filaments, on the order of 10 h −1 Mpc , have also been reported with tSZ observations (Tanimura et al 2019;de Graaff et al 2019). Longer cosmic filaments on scales of 30-100 Mpc were studied in (Tanimura et al 2020, hereafter T20) using the catalog of filaments detected in the Sloan Digital Sky Survey (SDSS) survey by Malavasi et al (2020) and hot gas was detected by stacking tSZ measurements. However, additional probes are necessary to break the degeneracy between the gas density and temperature.…”

“…Following T20, we studied the filaments identified in Malavasi et al (2020) using the Discrete Persistent Structure Extractor (DisPerSE) algorithm (Sousbie 2011). This method computes the gradient of a density field and, where the gradient is zero, identifies critical points (maxima, minima, saddles, and bifurcations).…”

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

“…From the galaxy distribution, DiPerSE first computes the density field using the Delaunay Tessellation Field Estimator (DTFE, Schaap & van de Weygaert 2000;van de Weygaert & Schaap 2009). To minimise the contamination by shot noise and to prevent the identification of small-scale, possibly spurious, features, following Malavasi et al (2020), we smoothed the density field by averaging the density value at each vertex of the Delaunay tessellation with the values at the surrounding vertices. The DisPerSE algorithm then identifies the critical points of the density field (points where the gradient of the field is zero) using discrete Morse theory.…”