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

Context. While cosmic voids are now recognized as a valuable cosmological probe, it is challenging to identify them in a galaxy catalog 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 nontrivial to estimate the detection significance and parameter uncertainties for individual voids. Aims. 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 and effectively represent 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. Methods. We used 50 posterior realizations of the large-scale structure in the Manticore-Local suite, obtained from the 2M++ galaxies, with z ≲ 0.1 and a sky area between ∼23 000 − 36 000 deg 2 . Running the VIDE void finder on each realization, we extracted 50 independent void catalogs. We performed a posterior clustering analysis to identify high-significance voids at the 5 σ level, and we assessed the probability distribution of their properties by combining the contributions of independent large-scale structure realizations. Results. We produced a publicly available catalog of 100 voids with a high statistical significance, including the probability distributions of the centers and the radii of the voids. We characterized the morphology of these regions and effectively produced a template for density environments that can be used in astrophysical applications such as galaxy evolution studies. Conclusions. 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 rigorously account for the observational systematics intrinsic to direct detection, and to provide a Bayesian characterization of their properties.