Journal Article

Ellipsoidal collapse and the redshift-space probability distribution function of dark matter

Tsz Yan Lam and Ravi K. Sheth

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 389, issue 3, pages 1249-1258
Published in print September 2008 | ISSN: 0035-8711
Published online September 2008 | e-ISSN: 1365-2966 | DOI:
Ellipsoidal collapse and the redshift-space probability distribution function of dark matter

Show Summary Details


We use the physics of ellipsoidal collapse to model the probability distribution function (PDF) of the smoothed dark matter density field in real and redshift space. We provide a simple approximation to the exact collapse model which clearly shows how the evolution can be thought of as a modification of the spherical evolution model as well as of the Zeldovich approximation. In essence, our model specifies how the initial smoothed overdensity and shear fields can be used to determine the shape and the size of the region at later times. We use our parametrization to extend previous work on the real-space PDF so that it predicts the redshift-space PDF as well. Our results are in good agreement with the measurements of the PDF in simulations of clustering from Gaussian initial conditions down to scales on which the rms fluctuation is slightly greater than unity. We also show how the highly non-Gaussian non-linear redshifted density field in a numerical simulation can be transformed so that it provides an estimate of the shape of the initial real-space PDF. When applied to our simulations, our method recovers the initial Gaussian PDF, provided the variance in the non-linear smoothed field is less than 4.

Keywords: methods: analytical; dark matter; large scale structure of the Universe

Journal Article.  5154 words.  Illustrated.

Subjects: Astronomy and Astrophysics

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.