Journal Article

The pairwise velocity probability density function in models with local primordial non-Gaussianity

Tsz Yan Lam, Takahiro Nishimichi and Naoki Yoshida

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 414, issue 1, pages 289-303
Published in print June 2011 | ISSN: 0035-8711
Published online June 2011 | e-ISSN: 1365-2966 | DOI: http://dx.doi.org/10.1111/j.1365-2966.2011.18390.x
The pairwise velocity probability density function in models with local primordial non-Gaussianity

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We study how primordial non-Gaussianity affects the pairwise velocity probability density function (PDF) using an analytical model and cosmological N-body simulations. We adopt the local type non-Gaussian models characterized by fnl, and examine both the linear velocity difference PDF and the linear pairwise velocity PDF. We show explicitly how fnl induces correlations between originally independent velocities along the parallel and the perpendicular to the line of separation directions. We compare the model results with measurements from N-body simulations of the non-Gaussian models. Linear theory fails to predict the PDF in the fnl models. Therefore, we develop an analytic model based on the Zeldovich approximation to describe the evolution of the velocity PDF. Our analytical model and simulation results show remarkably good agreement in both the parallel and the perpendicular directions for the PDF profiles, as well as the change in the PDF due to primordial non-Gaussianity. The agreement is particularly good for relatively small separations (<10 h−1 Mpc). The inclusion of the evolution of the velocity PDF is important to obtain a good description on the signature of primordial non-Gaussianity in the PDF. Our model provides the foundation to constrain fnl using the peculiar velocity in future surveys.

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

Journal Article.  8309 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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