A Bayesian catalog of 100 high-significance voids in the Local Universe

TL;DR

This paper presents a Bayesian method to identify and characterize high-significance cosmic voids in the local universe using constrained simulations and galaxy data, providing a detailed catalog and probabilistic properties.

Contribution

It introduces a Bayesian framework for void detection that accounts for observational biases and uncertainties, producing a statistically robust void catalog from galaxy surveys.

Findings

Produced a catalog of 100 high-significance voids.

Demonstrated the effectiveness of Bayesian analysis in cosmic void identification.

Provided probability distributions for void properties.

Abstract

While cosmic voids are now recognized as a valuable cosmological probe, identifying them in a galaxy catalog is challenging for multiple reasons: observational effects such as holes in the mask or magnitude selection hinder the detection process; galaxies are biased tracers of the underlying dark matter distribution; and it is non-trivial to estimate the detection significance and parameter uncertainties for individual voids. Our goal is to extract a catalog of voids from constrained simulations of the large-scale structure that are consistent with the observed galaxy positions, effectively representing statistically independent realizations of the probability distribution of the cosmic web. This allows us to carry out a full Bayesian analysis of the structures emerging in the Universe. We use 50 posterior realizations of the large-scale structure in the Manticore-Local suite, obtained from the 2M++ galaxies. Running the VIDE void finder on each realization, we extract 50 independent void catalogs. We perform a posterior clustering analysis to identify high-significance voids at the 5$\sigma$ level, and we assess the probability distribution of their properties. We produce a catalog of 100 voids with high statistical significance, available at https://voids.cosmictwin.org/, including the probability distributions of the centers and radii of the voids. We characterize the morphology of these regions, effectively producing a template for density environments that can be used in astrophysical applications such as galaxy evolution studies. While providing the community with a detailed catalog of voids in the nearby Universe, this work also constitutes an approach to identifying cosmic voids from galaxy surveys that allows us to account rigorously for the observational systematics intrinsic to direct detection, and provide a Bayesian characterization of their properties.