Why cosmic voids matter: nonlinear structure &amp; linear dynamics

Schuster, Nico; Hamaus, Nico; Dolag, K.; Weller, J.

doi:10.1088/1475-7516/2023/05/031

Cited by 10 publications

(11 citation statements)

References 138 publications

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“…The density profiles of voids traced by biased objects are deeper than those traced by dark matter particles. The radius corresponding to a fixed internal spherical density contrast is consequently larger for the former (e.g., Sutter et al 2014a;Pollina et al 2017;Schuster et al 2023). To include this effect in the void size function theory, Contarini et al (2019) proposed a parameterization of the Vdn model nonlinear underdensity threshold as a function of the tracer effective bias, b eff :…”

Section: Vdn Model Parameterizationmentioning

confidence: 99%

“…The number of cosmic voids identified by the algorithm is strictly related to both the survey volume and the number density of the objects used to trace voids (see, e.g., Schuster et al 2023). We report in the left panel of Figure 1 the number density of galaxies computed in concentric redshift shells, n gal (z), for both BOSS and PATCHY samples.…”

Section: Data Preparationmentioning

confidence: 99%

“…In recent years it has been explored in detail through cosmological simulations (e.g., Pisani et al 2015a;Pollina et al 2017;Contarini et al 2019Contarini et al , 2021Contarini et al , 2022Ronconi et al 2019;Verza et al 2019;Pelliciari et al 2023) and more rarely with real survey catalogs (Nadathur 2016;Sahlén et al 2016;Pollina et al 2019). Another fundamental and extensively studied void statistic is the void-galaxy crosscorrelation function, which represents an equivalent of the void density profile (Hamaus et al 2014b;Schuster et al 2023). It has been exploited for cosmological purposes by means of modeling the geometric and dynamic effects on the average void shape (Lavaux & Wandelt 2012;Pisani et al 2014;Cai et al 2016;Hamaus et al 2016Hamaus et al , 2017Hamaus et al , 2020Hamaus et al , 2022Hawken et al 2017Hawken et al , 2020Achitouv 2019;Correa et al 2019;Aubert et al 2022;Nadathur et al 2020Nadathur et al , 2022Correa et al 2022;Woodfinden et al 2022).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cosmological Constraints from the BOSS DR12 Void Size Function

Contarini,

Pisani,

Hamaus

et al. 2023

ApJ

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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.

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Section: Vdn Model Parameterizationmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cosmological Constraints from the BOSS DR12 Void Size Function

Contarini,

Pisani,

Hamaus

et al. 2023

ApJ

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“…Indeed, in addition to regions of excess energy density, ongoing studies probe cosmological models through observations of the dynamics and distribution of voids and 'supervoids'. Similarly, predictive features for the new theory with such a negative energy contribution might hence be testable, and potentially help account for any particularly large or deep voids or even apparent anomalies such as the CMB 'Cold Spot', which appears to be of primordial origin (see for example [74,75,76,77,78,79]).…”

Section: (With the Gauge Bosons Amentioning

confidence: 99%

The Dark Energy Sector of Generalised Proper Time

Jackson¹

2022

Preprint

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Generalisation from the local 4-dimensional spacetime form for a proper time interval provides an alternative basis for a unified theory contrasting with the more familiar approach of appending extra dimensions of space. In both cases the main goal is to derive a structure of matter that accounts for empirical observations from the properties of the additional components. Previous work has focussed upon the direct and significant connections that this new approach makes with the Standard Model of particle physics while also providing a link with dark matter models. In this paper we elaborate on how a further sector of generalised proper time, incorporating the key component of negative pressure, can account for the apparent dark energy fuelling the accelerating large-scale expansion of the universe. Through comparison with existing dark energy models we elucidate the elementary composition of this sector, its potential relation to an effective cosmological constant term, and the nature of possible interaction with the visible and dark matter sectors.

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“…Two void properties deserve special attention: (1) the ellipticity and (2) the density contrast (Biswas et al 2010;Lavaux & Wandelt 2010;Sutter et al 2014aSutter et al , 2015Kreisch et al 2022;Schuster et al 2023). In this work, we intend to use them, plus the traditional void radius, to train neural networks to perform likelihood-free inference on the value of the cosmological parameters.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning Cosmology from Void Properties

Wang 汪,

Pisani,

Villaescusa-Navarro

et al. 2023

ApJ

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Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine-learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES data set, where every catalog contains an average of 11,000 voids from a volume of 1 h − 1 Gpc 3 . We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train (1) fully connected neural networks on histograms from individual void properties and (2) deep sets from void catalogs to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of Ω m , σ 8, and n s with mean relative errors of 10%, 4%, and 3%, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.

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Why cosmic voids matter: nonlinear structure & linear dynamics

Cited by 10 publications

References 138 publications

Cosmological Constraints from the BOSS DR12 Void Size Function

Cosmological Constraints from the BOSS DR12 Void Size Function

The Dark Energy Sector of Generalised Proper Time

Machine-learning Cosmology from Void Properties

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