English

A halo bias function measured deeply into voids without stochasticity

Cosmology and Nongalactic Astrophysics 2014-05-06 v2

Abstract

We study the relationship between dark-matter haloes and matter in the MIP NN-body simulation ensemble, which allows precision measurements of this relationship, even deeply into voids. What enables this is a lack of discreteness, stochasticity, and exclusion, achieved by averaging over hundreds of possible sets of initial small-scale modes, while holding fixed large-scale modes that give the cosmic web. We find (i) that dark-matter-halo formation is greatly suppressed in voids; there is an exponential downturn at low densities in the otherwise power-law matter-to-halo density bias function. Thus, the rarity of haloes in voids is akin to the rarity of the largest clusters, and their abundance is quite sensitive to cosmological parameters. The exponential downturn appears both in an excursion-set model, and in a model in which fluctuations evolve in voids as in an open universe with an effective Ωm\Omega_m proportional to a large-scale density. We also find that (ii) haloes typically populate the average halo-density field in a super-Poisson way, i.e. with a variance exceeding the mean; and (iii) the rank-order-Gaussianized halo and dark-matter fields are impressively similar in Fourier space. We compare both their power spectra and cross-correlation, supporting the conclusion that one is roughly a strictly-increasing mapping of the other. The MIP ensemble especially reveals how halo abundance varies with `environmental' quantities beyond the local matter density; (iv) we find a visual suggestion that at fixed matter density, filaments are more populated by haloes than clusters.

Keywords

Cite

@article{arxiv.1309.6641,
  title  = {A halo bias function measured deeply into voids without stochasticity},
  author = {Mark C. Neyrinck and Miguel A. Aragon-Calvo and Donghui Jeong and Xin Wang},
  journal= {arXiv preprint arXiv:1309.6641},
  year   = {2014}
}

Comments

Changed to version accepted by MNRAS

R2 v1 2026-06-22T01:34:05.420Z