Cosmological Constraints from the BOSS DR12 Void Size Function

Abstract

We present the first cosmological constraints derived from the analysis of the void size function. This work relies on the final Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 (DR12) data set, a large spectroscopic galaxy catalog, ideal for the identification of cosmic voids. We extract a sample of voids from the distribution of galaxies, and we apply a cleaning procedure aimed at reaching high levels of purity and completeness. We model the void size function by means of an extension of the popular volume-conserving model, based on two additional nuisance parameters. Relying on mock catalogs specifically designed to reproduce the BOSS DR12 galaxy sample, we calibrate the extended size function model parameters and validate the methodology. We then apply a Bayesian analysis to constrain the Lambda cold dark matter (ΛCDM) model and one of its simplest extensions, featuring a constant dark energy equation of state parameter, w. Following a conservative approach, we put constraints on the total matter density parameter and the amplitude of density fluctuations, finding Ωm = 0.29 ± 0.06 and σ 8 = 0.79 − 0.08 + 0.09 . Testing the alternative scenario, we derive w = −1.1 ± 0.2, in agreement with the ΛCDM model. These results are independent and complementary to those derived from standard cosmological probes, opening up new ways to identify the origin of potential tensions in the current cosmological paradigm.

Machine-learning Cosmology from Void Properties

“…been used to provide constraints from data that showcase the constraining power of voids. The void-galaxy cross-correlation function has been used to constrain Ω m (Hamaus et al 2020) constraints (Contarini et al 2023). We note that there are several caveats in this work.…”

“…Cosmic voids, the underdense regions in the galaxy distribution, are dominated by dark energy and account for most of the volume of the Universe (Gregory & Thompson 1978;Jõeveer et al 1978;Kirshner et al 1981;Zeldovich et al 1982;van de Weygaert & van Kampen 1993;Bond et al 1996;Tikhonov & Karachentsev 2006). Thanks to their underdense feature, voids are particularly sensitive to cosmological information (Park & Lee 2007;Lavaux & Wandelt 2010;Bos et al 2012;Pisani et al 2015Pisani et al , 2019Hamaus et al 2016;Mao et al 2017;Chan et al 2019;Ronconi et al 2019;Sahlén 2019;Verza et al 2019;Bayer et al 2021;Kreisch et al 2022;Wilson & Bean 2021;Contarini et al 2022Contarini et al , 2023Pelliciari et al 2023). Until recently, void numbers were relatively low given the fact that voids are large regions and that the volume of surveys was relatively small.…”

“…In particular, we aim to constrain the cosmological parameters {Ω m , Ω b , h, n s , σ 8 } from either histograms of void properties (e.g., the distribution of void ellipticities) or from void catalogs directly. With respect to standard statistics (e.g., the power spectrum), void properties may have different degeneracies when inferring the value of parameters, so their usage may help break those degeneracies and get tighter constraints (Bayer et al 2021;Kreisch et al 2022;Pelliciari et al 2023;Contarini et al 2023).…”

“…Kreisch et al (2022) carried out an initial proof-of-concept analysis, training neural networks to perform likelihood-free inference using the void size function. The void size function is one of the main void statistics expected to reach its golden era in the next few years (Pisani et al 2015(Pisani et al , 2019Contarini et al 2022), with a theoretical model recently reaching a high maturity level (Contarini et al 2023). Cranmer et al (2021) used deep sets to extract information from the three void properties we consider in this paper.…”

Renormalizing one-point probability distribution function for cosmological counts in cells

“…Yet one more promising route is modeling of the lensing shear and convergence PDF [61,93]. Finally, counts-in-cells are closely related to the k-nearest-neighbor [94,95] and the void size [96] distribution functions. We believe our work sets firm grounds for analytical understanding of these interesting observables.…”