Excursion Sets and Non-Gaussian Void Statistics

Details Excursion Sets and Non-Gaussian Void Statistics Excursion Sets and Non-Gaussian Void Statistics

2010-11-04

arxiv.org/abs/1011.1229v2

Phys.Rev.D83:023521,2011

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

28 pages (18+appendices), 7 figures; v2 -- minor changes in sec 3.2, version published in PRD

Scientific paper

10.1103/PhysRevD.83.023521

Primordial non-Gaussianity (NG) affects the large scale structure (LSS) of the universe by leaving an imprint on the distribution of matter at late times. Much attention has been focused on using the distribution of collapsed objects (i.e. dark matter halos and the galaxies and galaxy clusters that reside in them) to probe primordial NG. An equally interesting and complementary probe however is the abundance of extended underdense regions or voids in the LSS. The calculation of the abundance of voids using the excursion set formalism in the presence of primordial NG is subject to the same technical issues as the one for halos, which were discussed e.g. in arXiv:1005.1203. However, unlike the excursion set problem for halos which involved random walks in the presence of one barrier $\delta_c$, the void excursion set problem involves two barriers $\delta_v$ and $\delta_c$. This leads to a new complication introduced by what is called the "void-in-cloud" effect discussed in the literature, which is unique to the case of voids. We explore a path integral approach which allows us to carefully account for all these issues, leading to a rigorous derivation of the effects of primordial NG on void abundances. The void-in-cloud issue in particular makes the calculation conceptually rather different from the one for halos. However, we show that its final effect can be described by a simple yet accurate approximation. Our final void abundance function is valid on larger scales than the expressions of other authors, while being broadly in agreement with those expressions on smaller scales.

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