The Supercluster-Void Network I. The Supercluster Catalogue and Large-Scale Distribution
Maret Einasto, Erik Tago, Jaak Jaaniste, Jaan Einasto, Heinz, Andernach

TL;DR
This study presents a new catalogue of superclusters and voids, revealing a regular large-scale cosmic network with a scale of about 120 Mpc, and suggests a higher mean space density of Abell clusters than previously estimated.
Contribution
The paper introduces a comprehensive supercluster catalogue extending to z=0.12, including 90 newly identified superclusters, and provides evidence for a regular supercluster-void network.
Findings
Identification of 220 superclusters, 90 of which are new.
Detection of a supercluster-void network with a scale of approximately 120 Mpc.
Proposed higher mean space density of Abell clusters at 2.6 x 10^-5 h^3 Mpc^-3.
Abstract
We investigate the distribution of superclusters and voids using a new catalogue of superclusters of rich clusters of galaxies which extends up to a redshift of z=0.12. The new catalogue contains 220 superclusters of rich clusters, of which 90 superclusters have been determined for the first time. Two thirds of those superclusters with eight or more member clusters are concentrated to a Dominant Supercluster Plane almost perpendicular to the plane of the Local Supercluster. Several independent methods indicate consistently the presence of a quite regular supercluster-void network with scale of approx. 120 Mpc. We determine the selection function of the sample of clusters and suggest that the mean true space density of Abell clusters is 2.6 x 10^-5 h^3 Mpc^-3, or twice the conventionally used value.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Computational Drug Discovery Methods · Complex Systems and Time Series Analysis
