Dark matter voids in the SDSS galaxy survey
Florent Leclercq, Jens Jasche, P.M. Sutter, Nico Hamaus, Benjamin, Wandelt

TL;DR
This paper presents a method to identify and analyze dark matter voids in the SDSS galaxy survey by reconstructing the large-scale structure using Bayesian inference and non-linear gravitational models, enabling high-precision void cosmology.
Contribution
It introduces a data-constrained reconstruction approach for dark matter voids, improving upon galaxy-based void catalogs by incorporating detailed structure and uncertainties.
Findings
Results agree with dark matter simulations for key void statistics.
Dark matter voids probe deeper hierarchy than galaxy-based voids.
Method enables high-precision void cosmology.
Abstract
What do we know about voids in the dark matter distribution given the Sloan Digital Sky Survey (SDSS) and assuming the model? Recent application of the Bayesian inference algorithm BORG to the SDSS Data Release 7 main galaxy sample has generated detailed Eulerian and Lagrangian representations of the large-scale structure as well as the possibility to accurately quantify corresponding uncertainties. Building upon these results, we present constrained catalogs of voids in the Sloan volume, aiming at a physical representation of dark matter underdensities and at the alleviation of the problems due to sparsity and biasing on galaxy void catalogs. To do so, we generate data-constrained reconstructions of the presently observed large-scale structure using a fully non-linear gravitational model. We then find and analyze void candidates using the VIDE toolkit. Our…
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