Updated void catalogs of the SDSS DR7 main sample

Abstract

We produce several public void catalogs using a volume-limited subsample of the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Using new implementations of three different void-finding algorithms, VoidFinder and two ZOBOV-based algorithms (VIDE and REVOLVER), we identify 1163, 531, and 518 cosmic voids with radii >10 Mpc/h, respectively, out to a redshift of z = 0.114 assuming a Planck 2018 cosmology, and 1184, 535, and 519 cosmic voids assuming a WMAP5 cosmology. We compute effective radii and centers for all voids and find none with an effective radius >54 Mpc/h. The median void effective radius is 15-19 Mpc/h for all three algorithms. We extract and discuss several properties of the void populations, including radial density profiles, the volume fraction of the catalog contained within voids, and the fraction of galaxies contained within voids. Using 64 mock galaxy catalogs created from the Horizon Run 4 N-body simulation, we compare simulated and observed void properties and find good agreement between the SDSS DR7 and mock catalog results.

14 pages, 6 figures, published in ApJS

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Cited by in corpus (12)

• CAVITY, Calar Alto Void Integral-field Treasury surveY and project extension
• Machine-learning cosmology from void properties
• An Observed Transition of Galaxy Spins on the Void Surfaces
• Cosmology from one galaxy in a void?
• Constraining cosmological parameters using void statistics from the SDSS survey
• DESIVAST: A Catalog of Low-Redshift Voids using Data from the DESI DR1 Bright Galaxy Survey
• Galaxy Superclusters and Their Complexes in the Cosmic Web
• Quantifying the Active Galactic Nuclei Fraction in Cosmic Voids via Mid-Infrared Variability
• Cosmology with Cosmic Voids
• Using Elliptical Galaxy Kinematics to Compare of the Strength of Gravity in Cosmological Regions of Differing Gravitational Potential -- A First Look
• Tracing the high-z cosmic web with Quaia: catalogues of voids and clusters in the quasar distribution
• DeepVoid: A Deep Learning Void Detector