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The probability distribution function (PDF) of the mass surface density is an essential characteristic of the structure of molecular clouds or the interstellar medium in general. Observations of the PDF of molecular clouds indicate a…

星系天体物理 · 物理学 2015-06-18 Joerg Fischera

One-point probability distribution functions (PDFs) of the cosmic matter density are powerful cosmological probes that extract non-Gaussian properties of the matter distribution and complement two-point statistics. Computing the covariance…

宇宙学与河外天体物理 · 物理学 2023-01-09 Cora Uhlemann , Oliver Friedrich , Aoife Boyle , Alex Gough , Alexandre Barthelemy , Francis Bernardeau , Sandrine Codis

Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors.…

天体物理仪器与方法 · 物理学 2022-05-31 Biprateep Dey , Jeffrey A. Newman , Brett H. Andrews , Rafael Izbicki , Ann B. Lee , David Zhao , Markus Michael Rau , Alex I. Malz

We derive a multifractal model for the velocity probability density distribution function (PDF), which is valid from the inertial range to the viscous range. The model gives a continuous evolution of velocity PDFs from large to small…

chao-dyn · 物理学 2008-02-03 Jens Eggers , Z. Jane Wang

Cosmological Perturbation Theory (PT) is a useful tool to study the cumulants of the density and velocity fields in the large scale structure of the Universe. In Papers I & II of this series we saw that the Spherical Collapse (SC) model…

天体物理学 · 物理学 2009-10-30 P. Fosalba , E. Gaztanaga

Perturbation Theory (PT) applied to a cosmological density field with Gaussian initial fluctuations suggests a specific hierarchy for the correlation functions when the variance is small. In particular quantitative predictions have been…

天体物理学 · 物理学 2009-10-30 D. Munshi , F. Bernardeau , A. L. Melott , R. Schaeffer

We study the possibility of using the entire probability distribution function (PDF) of the aperture mass Map and its related cumulative probability distribution function (CPDF) to obtain meaningful constraints on cosmological parameters.…

天体物理学 · 物理学 2007-05-23 Dipak Munshi , Patrick Valageas

We measure the matter probability distribution function (PDF) via counts in cells in a volume limited subsample of the Sloan Digital Sky Survey Luminous Red Galaxy Catalog on scales from $30 h^{-1}$Mpc to $150 h^{-1}$Mpc and estimate the…

宇宙学与河外天体物理 · 物理学 2010-12-16 Péter Pápai , István Szapudi

We study the effect of the non-Gaussianity induced by gravitational evolution upon the statistical properties of absorption in quasar (QSO) spectra. Using the generic hierarchical ansatz and the lognormal approximation we derive the…

宇宙学与河外天体物理 · 物理学 2015-06-03 Dipak Munshi , Peter Coles , Matteo Viel

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible it is necessary to go beyond two-point statistics. Rather than going…

宇宙学与河外天体物理 · 物理学 2022-09-08 Alex Gough , Cora Uhlemann

We investigate the form of the one-point probability distribution function (pdf) for the density field of the interstellar medium using numerical simulations that successively reduce the number of physical processes included.…

天体物理学 · 物理学 2014-10-13 J. Scalo , E. Vazquez-Semadeni , D. Chappell , T. Passot

We study density fluctuations in supersonic turbulence using both theoretical methods and numerical simulations. A theoretical formulation is developed for the probability distribution function (PDF) of the density at steady state,…

星系天体物理 · 物理学 2019-09-04 Liubin Pan , Paolo Padoan , Åke Nordlund

Cosmological density fields are assumed to be translational and rotational invariant, avoiding any special point or direction, thus satisfying the Copernican Principle. A spatially inhomogeneous matter distribution can be compatible with…

宇宙学与河外天体物理 · 物理学 2015-05-19 Francesco Sylos Labini , Yuri V. Baryshev

Basing our discussion on the Lagrangian description of hydrodynamics, we studied the evolution of density fluctuation for nonlinear cosmological dynamics. Adhesion approximation (AA) is known as a phenomenological model that describes the…

天体物理学 · 物理学 2009-11-10 Takayuki Tatekawa

Methods. We perform numerical simulations of the evolution of the cosmic web for the conventional LCDM model. The simulations cover a wide range of box sizes L = 256 - 4000 Mpc/h, mass and force resolutions and epochs from very early…

宇宙学与河外天体物理 · 物理学 2021-08-18 Jaan Einasto , Anatoly Klypin , Gert Hütsi , L. J. Liivamägi , Maret Einasto

We study the statistical properties of the gravitational field generated by galaxy distribution observed bythe Sloan Digital Sky Survey (DR7). We characterize the probability density function of gravitational force fluctuations and relate…

宇宙学与河外天体物理 · 物理学 2015-05-19 Francesco Sylos Labini

In this paper I consider the nonlinear evolution of a rare density fluctuation in a random density field with Gaussian fluctuations, and I rigorously show that it follows the spherical collapse dynamics applied to its mean initial profile.…

天体物理学 · 物理学 2009-10-22 F. Bernardeau

We have discovered analytical expressions for the probability density function (PDF) of photons that are multiply scattered in relativistic flows, under the assumption of isotropic and inelastic scattering. These expressions characterize…

Intermittency in MHD turbulence has been analyzed using high resolution 2D numerical simulations. We show that the Probability Distribution Functions (PDFs) of the fluctuations of the Elsasser fields, magnetic field and velocity field…

混沌动力学 · 物理学 2015-06-26 L. Sorriso-Valvo , V. Carbone , P. Veltri , H. Politano , A. Pouquet

We consider one-dimensional hyperbolic PDEs, linear and nonlinear, with random initial data. Our focus is the {\em pointwise statistics,} i.e., the probability measure of the solution at any fixed point in space and time. For linear…

偏微分方程分析 · 数学 2025-12-17 Alina Chertock , Pierre Degond , Amir Sagiv , Li Wang