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A robust public catalogue of voids and superclusters in the SDSS Data Release 7 galaxy surveys

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  • Seshadri Nadathur
  • Shaun Hotchkiss
The study of the interesting cosmological properties of voids in the Universe depends on the efficient and robust identification of such voids in galaxy redshift surveys. Recently, Sutter et al. have published a public catalogue of voids in the Sloan Digital Sky Survey Data Release 7 main galaxy and luminous red galaxy samples, using the void-finding algorithm ZOBOV, which is based on the watershed transform. We examine the properties of this catalogue and show that it suffers from several problems and inconsistencies, including the identification of some extremely overdense regions as voids. As a result, cosmological results obtained using this catalogue need to be reconsidered. We provide instead an alternative, self-consistent, public catalogue of voids in the same galaxy data, obtained from using an improved version of the same watershed transform algorithm. We provide a more robust method of dealing with survey boundaries and masks, as well as with a radially varying selection function, which means that our method can be applied to any other survey. We discuss some basic properties of the voids thus discovered, and describe how further information may be obtained from the catalogue. In addition, we apply an inversion of the algorithm to the same data to obtain a corresponding catalogue of large-scale overdense structures, or ‘superclusters’. Our catalogues are available for public download on the journal website.
Original languageEnglish
Pages (from-to)1248-1262
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
Early online date19 Mar 2014
Publication statusPublished - 11 May 2014


  • A robust public catalogue of voids

    Rights statement: This article has been accepted for publication in Monthly Notices of the Astronomical Society ©: 2014 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

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