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2020
DOI: 10.1093/mnras/staa320
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A comparison of cosmological filaments catalogues

Abstract: In this work we compare three catalogues of cosmological filaments identified in the Sloan Digital Sky Survey by means of different algorithms by Tempel et al., Pereyra et al., and Martínez et al. We analyse how different identification techniques determine differences in the filament statistical properties: length, elongation, redshift distribution, and abundance. We find that the statistical properties of the filaments strongly depend on the identification algorithm. We use a volume limited sample of galax… Show more

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Cited by 36 publications
(34 citation statements)
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References 55 publications
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“…While a comparison of these catalogues is beyond the goal of this work (see e.g. Rost et al 2020) also because very different algorithms were employed, we still qualitatively compare our work with others from the literature. While the sky distribution of filaments appear to be visually similar (see e.g.…”
Section: Qualitative Comparison With the Literaturementioning
confidence: 99%
See 1 more Smart Citation
“…While a comparison of these catalogues is beyond the goal of this work (see e.g. Rost et al 2020) also because very different algorithms were employed, we still qualitatively compare our work with others from the literature. While the sky distribution of filaments appear to be visually similar (see e.g.…”
Section: Qualitative Comparison With the Literaturementioning
confidence: 99%
“…Surveys such as the Two-Degree Field Galaxy Redshift Survey (2dFGRS, Colless et al 2001), the Sloan Digital Sky Survey (SDSS, York et al 2000), the Galaxy And Mass Assembly survey (GAMA, Driver et al 2009), the Vimos Public Extragalactic Redshift Survey (VIPERS, Scodeggio et al 2018), or the COSMOS survey (Scoville et al 2007) have allowed us to obtain statistical samples of filaments and other LSS features. For example, Chen et al (2016) and Tempel et al (2014a) have produced filament catalogues in the SDSS (but see also the works by Aragón Calvo 2007;Sousbie et al 2011;Rost et al 2020;Shuntov et al 2020;Kraljic et al 2020, some of which also used the same algorithm as we used here). Other works such as Kraljic et al (2018) and Alpaslan et al (2014) detected filaments in GAMA, while Malavasi et al (2017) detected filaments in VIPERS.…”
Section: Introductionmentioning
confidence: 99%
“…To add to the difficulty, different filament-finding algorithms are based on different implicit definitions of these objects. This leads to wide-ranging discrepancies in relation to the nature of identified objects found by the different filament finders (Libeskind et al 2018;Rost et al 2020), including DISPERSE (Sousbie 2011), Semita (Pereyra et al 2019), Nexus (Aragón-Calvo et al 2007;Cautun, van de Weygaert & Jones 2013), or Bisous (Tempel et al 2014). Despite these drawbacks, understanding the large-scale structures of the Universe, and how the galaxies they host evolve, remain key science challenges.…”
mentioning
confidence: 99%
“…Velocity flows in these exceptional regions in the Universe deserve a thorough discussion. In Rost et al (2020a) we do exactly that and investigate velocity flows of gas and dark matter around clusters in much greater detail, using the same simulations.…”
Section: The Velocity Field Of Filament Galaxiesmentioning
confidence: 99%
“…Filaments connected to clusters are only one aspect of a complex multiscale picture that encompasses thick filaments as well as thin tendrils on scales of a few Mpc up to 100 Mpc and more, as well as sheetlike membranes easily mistaken as filaments in projection. The last decade has seen a number of excellent methods to identify and classify features of the cosmic web (e.g., Aragón-Calvo et al 2007;Sousbie 2011;Cautun et al 2012;Courtois et al 2013;Tempel et al 2014;Falck & Neyrinck 2015), each designed to tackle specific problems to be applied to different kinds of data and thus disagreements are understandable and well documented (e.g., Libeskind et al 2017;Rost et al 2020b). Structure finding methods are successfully being applied to simulated and observed datasets alike, including photometric and spectroscopic surveys such as SDSS (Tempel et al 2013;Kuutma et al 2017;Chen et al 2016;Malavasi et al 2020), COS-MOS (Darvish et al 2017;Laigle et al 2017) and GAMA (Kraljic et al 2018;Welker et al 2019) amongst others (Malavasi et al 2016;Sarron et al 2019;Santiago-Bautista et al 2020).…”
Section: Introductionmentioning
confidence: 99%