A New Statistical Perspective to the Cosmic Void Distribution

Pycke, Jean-Renaud; Russell, Esra

doi:10.3847/0004-637x/821/2/110

Cited by 11 publications

(31 citation statements)

References 57 publications

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“…In the raw void size distributions, an unexpected local peak is observed around the value 20 Mpc/h in the DM Full sample and in the DM Dense sample around 27 Mpc/h, see Fig 1 Upper Left and Lower Left Panels. A similar behavior is observed by Pycke & Russell (2016) in the N-body Mock sample around the value 50 Mpc/h.…”

Section: Statistical Properties Of Void Distributionssupporting

confidence: 84%

“…Note that a leptokurtic type distribution is characterized by a high degree of peakedness (see Sheskin (2011) p. 16-30 for more details). The same result of positively skewed and leptokurtic void size distributions is also pointed out by Pycke & Russell (2016) for three samples of CVC.…”

Section: Statistical Properties Of Void Distributionssupporting

confidence: 77%

“…The three main catalogs under study are dark matter, halo and galaxy catalogs. Therefore, we confirm that the system of 3-parameter log-normal distribution obtained by Pycke & Russell (2016) provides a fairly satisfactory model of the size distribution of voids. In addition to this, we obtain equations which satisfy linear relations between maximum tree depth and the shape parameters of the void size distribution as proposed by Pycke & Russell (2016).…”

Section: Introductionsupporting

confidence: 79%

“…In the previous study, Pycke & Russell (2016) show that HOD Sparse and HOD Dense samples also present single population void size distributions. Note that our goal is not to compare the invariant distributions in Fig 2, but to find the reason of the emergence of the second population (Fig 1) versus single population in the void size distributions of CVC.…”

Section: Statistical Properties Of Void Distributionsmentioning

confidence: 85%

“…Note that the void size distributions for HOD Dense, HOD Sparse and N-body mock data sets are discussed in great detail in Pycke & Russell (2016). In the raw void size distributions, an unexpected local peak is observed around the value 20 Mpc/h in the DM Full sample and in the DM Dense sample around 27 Mpc/h, see Fig 1 Upper Left and Lower Left Panels.…”

Section: Statistical Properties Of Void Distributionsmentioning

confidence: 94%

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Log-Normal Distribution of Cosmic Voids in Simulations and Mocks

Russell¹,

Pycke

2017

ApJ

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Following up on previous studies, we here complete a full analysis of the void size distributions of the Cosmic Void Catalog (CVC) based on three different simulation and mock catalogs; dark matter, haloes and galaxies. Based on this analysis, we attempt to answer two questions: Is a 3-parameter log-normal distribution a good candidate to satisfy the void size distributions obtained from different types of environments? Is there a direct relation between the shape parameters of the void size distribution and the environmental effects? In an attempt to answer these questions, we here find that all void size distributions of these data samples satisfy the 3-parameter log-normal distribution whether the environment is dominated by dark matter, haloes or galaxies. In addition, the shape parameters of the 3-parameter log-normal void size distribution seem highly affected by environment, particularly existing substructures. Therefore, we show two quantitative relations given by linear equations between the skewness and the maximum tree depth, and variance of the void size distribution and the maximum tree depth directly from the simulated data. In addition to this, we find that the percentage of the voids with nonzero central density in the data sets has a critical importance. If the number of voids with nonzero central densities reaches ≥ %3.84 in a simulation/mock sample, then a second population is observed in the void size distributions. This second population emerges as a second peak in the log-normal void size distribution at larger radius.

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Section: Statistical Properties Of Void Distributionssupporting

confidence: 84%

Section: Statistical Properties Of Void Distributionssupporting

confidence: 77%

Section: Introductionsupporting

confidence: 79%

Section: Statistical Properties Of Void Distributionsmentioning

confidence: 85%

Section: Statistical Properties Of Void Distributionsmentioning

confidence: 94%

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Log-Normal Distribution of Cosmic Voids in Simulations and Mocks

Russell¹,

Pycke

2017

ApJ

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Ridges in the Dark Energy Survey for cosmic trough identification

Moews

Schmitz

Lawler

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures enables us to compare the standard model with alternative cosmologies, constrain the dark energy equation of state, and distinguish between different gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing observables to recover the large-scale structure, paving the way for a more versatile and effective search for troughs.

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On the linearity of tracer bias around voids

Pollina

Hamaus

Dolag

et al. 2017

Monthly Notices of the Royal Astronomical Society

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The large-scale structure of the universe can only be observed via luminous tracers of the dark matter. However, the clustering statistics of tracers are biased and depend on various properties, such as their host-halo mass and assembly history. On very large scales this tracer bias results in a constant offset in the clustering amplitude, known as linear bias. Towards smaller non-linear scales, this is no longer the case and tracer bias becomes a complicated function of scale and time. We focus on tracer bias centred on cosmic voids, depressions of the density field that spatially dominate the universe. We consider three types of tracers: galaxies, galaxy clusters and AGN, extracted from the hydrodynamical simulation Magneticum Pathfinder. In contrast to common clustering statistics that focus on auto-correlations of tracers, we find that void-tracer cross-correlations are successfully described by a linear-bias relation. The tracerdensity profile of voids can thus be related to their matter-density profile by a single number. We show that it coincides with the linear tracer bias extracted from the large-scale autocorrelation function and expectations from theory, if sufficiently large voids are considered. For smaller voids we observe a shift towards higher values. This has important consequences on cosmological parameter inference, as the problem of unknown tracer bias is alleviated up to a constant number. The smallest scales in existing datasets become accessible to simpler models, providing numerous modes of the density field that have been disregarded so far, but may help to further reduce statistical errors in constraining cosmology.

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A New Statistical Perspective to the Cosmic Void Distribution

Cited by 11 publications

References 57 publications

Log-Normal Distribution of Cosmic Voids in Simulations and Mocks

Log-Normal Distribution of Cosmic Voids in Simulations and Mocks

Ridges in the Dark Energy Survey for cosmic trough identification

On the linearity of tracer bias around voids

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