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Register a new userA halo bias function measured deeply into voids without stochasticity
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Mark C. Neyrinck
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We study the relationship between dark-matter haloes and matter in the MIP $N$-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 $Omega_m$ 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.
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A very large dynamic range with simultaneous capture of both large- and small-scales in the simulations of cosmic structures is required for correct modelling of many cosmological phenomena, particularly at high redshift. This is not always available, or when it is, it makes such simulations very expensive. We present a novel sub-grid method for modelling low-mass ($10^5,M_odotleq M_{rm halo}leq 10^9,M_odot$) haloes, which are otherwise unresolved in large-volume cosmological simulations limited in numerical resolution. In addition to the deterministic halo bias that captures the average property, we model its stochasticity that is correlated in time. We find that the instantaneous binned distribution of the number of haloes is well approximated by a log-normal distribution, with overall amplitude modulated by this temporal correlation bias. The robustness of our new scheme is tested against various statistical measures, and we find that temporally correlated stochasticity generates mock halo data that is significantly more reliable than that from temporally uncorrelated stochasticity. Our method can be applied for simulating processes that depend on both the small- and large-scale structures, especially for those that are sensitive to the evolution history of structure formation such as the process of cosmic reionization. As a sample application, we generate a mock distribution of medium-mass ($ 10^{8} leq M/M_{odot} leq 10^{9}$) haloes inside a 500 Mpc$,h^{-1}$, $300^3$ grid simulation box. This mock halo catalogue bears a reasonable statistical agreement with a halo catalogue from numerically-resolved haloes in a smaller box, and therefore will allow a very self-consistent sets of cosmic reionization simulations in a box large enough to generate statistically reliable data.
Understanding the biasing between the clustering properties of halos and the underlying dark matter distribution is important for extracting cosmological information from ongoing and upcoming galaxy surveys. While on sufficiently larges scales the halo overdensity is a local function of the mass density fluctuations, on smaller scales the gravitational evolution generates non-local terms in the halo density field. We characterize the magnitude of these contributions at third-order in perturbation theory by identifying the coefficients of the non-local invariant operators, and extend our calculation to include non-local (Lagrangian) terms induced by a peak constraint. We apply our results to describe the scale-dependence of halo bias in cosmologies with massive neutrinos. The inclusion of gravity-induced non-local terms and, especially, a Lagrangian $k^2$-contribution is essential to reproduce the numerical data accurately. We use the peak-background split to derive the numerical values of the various bias coefficients from the excursion set peak mass function. For neutrino masses in the range $0leq sum_i m_{ u_i} leq 0.6$ eV, we are able to fit the data with a precision of a few percents up to $k=0.3, h {rm ,Mpc^{-1}}$ without any free parameter.
To study the impact of sparsity and galaxy bias on void statistics, we use a single large-volume, high-resolution N-body simulation to compare voids in multiple levels of subsampled dark matter, halo populations, and mock galaxies from a Halo Occupation Distribution model tuned to different galaxy survey densities. We focus our comparison on three key observational statistics: number functions, ellipticity distributions, and radial density profiles. We use the hierarchical tree structure of voids to interpret the impacts of sampling density and galaxy bias, and theoretical and empirical functions to describe the statistics in all our sample populations. We are able to make simple adjustments to theoretical expectations to offer prescriptions for translating from analytics to the void properties measured in realistic observations. We find that sampling density has a much larger effect on void sizes than galaxy bias. At lower tracer density, small voids disappear and the remaining voids are larger, more spherical, and have slightly steeper profiles. When a proper lower mass threshold is chosen, voids in halo distributions largely mimic those found in galaxy populations, except for ellipticities, where galaxy bias leads to higher values. We use the void density profile of Hamaus et al. (2014) to show that voids follow a self-similar and universal trend, allowing simple translations between voids studied in dark matter and voids identified in galaxy surveys. We have added the mock void catalogs used in this work to the Public Cosmic Void Catalog at http://www.cosmicvoids.net.
The simplest stochastic halo formation models assume that the traceless part of the shear field acts to increase the initial overdensity (or decrease the underdensity) that a protohalo (or protovoid) must have if it is to form by the present time. Equivalently, it is the difference between the overdensity and (the square root of the) shear that must be larger than a threshold value. To estimate the effect this has on halo abundances using the excursion set approach, we must solve for the first crossing distribution of a barrier of constant height by the random walks associated with the difference, which is now (even for Gaussian initial conditions) a non-Gaussian variate. The correlation properties of such non-Gaussian walks are inherited from those of the density and the shear, and, since they are independent processes, the solution is in fact remarkably simple. We show that this provides an easy way to understand why earlier heuristic arguments about the nature of the solution worked so well. In addition to modelling halos and voids, this potentially simplifies models of the abundance and spatial distribution of filaments and sheets in the cosmic web.
We study the impact of theoretical uncertainty in the dark matter halo mass function and halo bias on dark energy constraints from imminent galaxy cluster surveys. We find that for an optical cluster survey like the Dark Energy Survey, the accuracy required on the predicted halo mass function to make it an insignificant source of error on dark energy parameters is ~ 1%. The analogous requirement on the predicted halo bias is less stringent (~ 5%), particularly if the observable-mass distribution can be well constrained by other means. These requirements depend upon survey area but are relatively insensitive to survey depth. The most stringent requirements are likely to come from a survey over a significant fraction of the sky that aims to observe clusters down to relatively low mass, Mth ~ 10^13.7 Msun/h; for such a survey, the mass function and halo bias must be predicted to accuracies of ~ 0.5% and ~ 1%, respectively. These accuracies represent a limit on the practical need to calibrate ever more accurate halo mass and bias functions. We find that improving predictions for the mass function in the low-redshift and low-mass regimes is the most effective way to improve dark energy constraints.
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