Volume 642, October 2020
|Number of page(s)||24|
|Section||Cosmology (including clusters of galaxies)|
|Published online||30 September 2020|
Characterising filaments in the SDSS volume from the galaxy distribution
Université Paris-Saclay, CNRS, Institut d’astrophysique spatiale, 91405 Orsay, France
Accepted: 31 July 2020
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).
Key words: large-scale structure of Universe / catalogs / galaxies: statistics / galaxies: distances and redshifts / methods: data analysis / galaxies: evolution
© N. Malavasi et al. 2020
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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