Halo assembly bias and the tidal anisotropy of the local halo environment
21 May 2018-Monthly Notices of the Royal Astronomical Society (Oxford Academic)-Vol. 476, Iss: 3, pp 3631-3647
TL;DR: In this article, the role of the local tidal environment in determining the assembly bias of dark matter haloes was studied, using correlations between the large-scale and small-scale environments of simulated haloes at z = 0 with masses between 10^11.6 and 10^14.9.
Abstract: We study the role of the local tidal environment in determining the assembly bias of dark matter haloes. Previous results suggest that the anisotropy of a halo's environment (i.e. whether it lies in a filament or in a more isotropic region) can play a significant role in determining the eventual mass and age of the halo. We statistically isolate this effect, using correlations between the large-scale and small-scale environments of simulated haloes at z = 0 with masses between 10^11.6 ≲ (m/h^−1 M_⊙) ≲ 10^14.9. We probe the large-scale environment, using a novel halo-by-halo estimator of linear bias. For the small-scale environment, we identify a variable α_R that captures the tidal anisotropy in a region of radius R = 4R_200b around the halo and correlates strongly with halo bias at fixed mass. Segregating haloes by α_R reveals two distinct populations. Haloes in highly isotropic local environments (α_R ≲ 0.2) behave as expected from the simplest, spherically averaged analytical models of structure formation, showing a negative correlation between their concentration and large-scale bias at all masses. In contrast, haloes in anisotropic, filament-like environments (α_R ≳ 0.5) tend to show a positive correlation between bias and concentration at any mass. Our multiscale analysis cleanly demonstrates how the overall assembly bias trend across halo mass emerges as an average over these different halo populations, and provides valuable insights towards building analytical models that correctly incorporate assembly bias. We also discuss potential implications for the nature and detectability of galaxy assembly bias.
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TL;DR: In this article, the mass, accretion rate, and formation time of dark matter halos near protofilaments are analytically predicted using a conditional version of the excursion set approach in its so-called upcrossing approximation.
Abstract: The mass, accretion rate, and formation time of dark matter haloes near protofilaments (identified as saddle points of the potential) are analytically predicted using a conditional version of the excursion set approach in its so-called upcrossing approximation. The model predicts that at fixed mass, mass accretion rate and formation time vary with orientation and distance from the saddle, demonstrating that assembly bias is indeed influenced by the tides imposed by the cosmic web. Starved, early-forming haloes of smaller mass lie preferentially along the main axis of filaments, while more massive and younger haloes are found closer to the nodes. Distinct gradients for distinct tracers such as typical mass and accretion rate occur because the saddle condition is anisotropic, and because the statistics of these observables depend on both the conditional means and their covariances. The theory is extended to other critical points of the potential field. The response of the mass function to variations of the matter density field (the so-called large-scale bias) is computed, and its trend with accretion rate is shown to invert along the filament. The signature of this model should correspond at low redshift to an excess of reddened galactic hosts at fixed mass along preferred directions, as recently reported in spectroscopic and photometric surveys and in hydrodynamical simulations. The anisotropy of the cosmic web emerges therefore as a significant ingredient to describe jointly the dynamics and physics of galaxies, e.g. in the context of intrinsic alignments or morphological diversity.
71 citations
TL;DR: In this paper, it was shown that the internal properties of dark matter haloes correlate with the large-scale halo clustering strength at fixed halo mass, and are also strongly affected by the local, non-linear cosmic web.
Abstract: The internal properties of dark matter haloes correlate with the large-scale halo clustering strength at fixed halo mass – an effect known as assembly bias – and are also strongly affected by the local, non-linear cosmic web. Characterizing a halo’s local web environment by its tidal anisotropy α at scales approximately four times the halo radius, we demonstrate that these multiscale correlations represent two distinct statistical links: one between the internal property and α, and the other between α and large-scale (|${\gtrsim}30\, h^{-1}\, {\rm Mpc}$|) halo bias b_1. We focus on scalar internal properties of haloes related to formation time (concentration c_vir), shape (mass ellipsoid asphericity c/a), velocity dispersion structure (velocity ellipsoid asphericity /a_v and velocity anisotropy β), and angular momentum (dimensionless spin λ) in the mass range |$8\times 10^{11}\lesssim M_{\rm vir}/(\, h^{-1}\, \mathrm{M}_{\odot })\lesssim 5\times 10^{14}$|. Using conditional correlation coefficients and other detailed tests, we show that the joint distribution of α, b_1, and any of the internal properties c ∈ {β, c_v/a_v, c/a, c_vir, λ} is consistent with p(α, b_1, c) ≃ p(α)p(b_1|α)p(c|α), at all but the largest masses. Thus, the assembly bias trends c↔b_1 reflect the two fundamental correlations c↔α and b_1↔α. Our results are unaffected by the exclusion of haloes with recent major merger events or splashback objects, although the latter are distinguished by the fact that α does not explain their assembly bias trends. The overarching importance of α provides a new perspective on the nature of assembly bias of distinct haloes, with potential ramifications for incorporating realistic assembly bias effects into mock catalogues of future large-scale structure surveys and for detecting galaxy assembly bias.
56 citations
Cites background or methods or result from "Halo assembly bias and the tidal an..."
...Although the variables are now standardized, their intrinsic correlations are not necessarily linear or even monotonic (see, e.g., Figure 12 of Paranjape et al. 2018a, which shows that the median halo concentration is non-monotonic in α at fixed mass), so one might still worry about systematic…...
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...…of the halo environment; a large fraction of low-mass haloes live in highly anisotropic and biased environments10 such as cosmic filaments, unlike more isolated haloes which dominate their environment and follow the trends predicted by standard spherical collapse models (Paranjape et al. 2018a)....
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...In fact, defining α at ∼ 4× the halo radius ensures that this correlation is stronger than that between b1 and the local overdensity δ of the halo environment measured at the same scale (Paranjape et al. 2018a)....
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...As our main indicator of a halo’s non-linear local environment, we will use the tidal anisotropy variable α introduced by Paranjape et al. (2018a)....
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...…order of a few virial radii) plays an important role in the assembly bias due to halo formation epoch (Hahn et al. 2009), mass accretion rate (Fakhouri & Ma 2010; Musso et al. 2018), internal velocity dispersion structure (Borzyszkowski et al. 2017) and halo concentration (Paranjape et al. 2018a)....
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TL;DR: In this paper, the authors present a detailed analysis of the physical processes that cause halo assembly bias and show that splashback subhaloes are responsible for two thirds of the assembly bias signal, but do not account for the entire effect.
Abstract: We present a detailed analysis of the physical processes that cause halo assembly bias -- the dependence of halo clustering on proxies of halo formation time. We focus on the origin of assembly bias in the mass range corresponding to the hosts of typical galaxies and use halo concentration as our chief proxy of halo formation time. We also repeat our key analyses across a broad range of halo masses and for alternative formation time definitions. We show that splashback subhaloes are responsible for two thirds of the assembly bias signal, but do not account for the entire effect. After splashback subhaloes have been removed, we find that the remaining assembly bias signal is due to a relatively small fraction ($\lesssim 10\%$) of haloes in dense regions. We test a number of additional physical processes thought to contribute to assembly bias and demonstrate that the two key processes are the slowing of mass growth by large-scale tidal fields and by the high velocities of ambient matter in sheets and filaments. We also rule out several other proposed physical causes of halo assembly bias. Based on our results, we argue that there are three processes that contribute to assembly bias of low-mass halos: large-scale tidal fields, gravitational heating due to the collapse of large-scale structures, and splashback subhaloes located outside the virial radius.
54 citations
Cites background or methods from "Halo assembly bias and the tidal an..."
...…growth can be caused by the overall tidal force from all of the surrounding haloes and structures in the matter distribution (Hahn et al. 2009; Wang et al. 2011; Paranjape et al. 2018; Musso et al. 2018), as the largest filaments and sheets generate strong tidal fields throughout their volumes....
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...Second, we carried out analysis of assembly bias described in sections 2.7 and 2.2 using αR and qR from Paranjape et al. (2018) and t from Wang et al. (2011) as proxies for the tidal anisotropy and found that all of these proxies were not as efficient at removing assembly bias as Rtidal....
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...…et al. 2008; Wang et al. 2009; Hahn et al. 2009; Wang et al. 2011; Li et al. 2013; Wetzel et al. 2014; Sunayama et al. 2016; Hearin et al. 2016b; Paranjape et al. 2018; Salcedo et al. 2018; Musso et al. 2018), their relative importance and a coherent physical picture for the origin of low-mass…...
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...Tidal forces have been proposed as a potential cause of assembly bias (Hahn et al. 2009; Wang et al. 2009; Hearin et al. 2016b; Salcedo et al. 2018; Paranjape et al. 2018) because they can slow down, stop, or reverse mass accretion....
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...Some authors have suggested that the primary feature of interest in the tidal field is its anisotropy, which can be defined in a number of ways (Wang et al. 2011; Paranjape et al. 2018)....
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TL;DR: ReliefFast as discussed by the authors finds that the bias induced by light relics partially compensates the suppression of power, and should be accounted for in any search for relics with galaxy data, at little computational cost.
Abstract: Cosmological data can be used to search for---and characterize---light particles in the standard model, if these populate our Universe. In addition to the well-known effect of these light relics in the background cosmology, usually parametrized through a change in the effective number $N_{\rm eff}$ of neutrino species, these particles can become nonrelativistic at later times, affecting the growth of matter fluctuations due to their thermal velocities. An extensively studied example is that of massive neutrinos, which are known to produce a suppression in the matter power spectrum due to their free streaming. Galaxies, as biased traces of matter fluctuations, can therefore provide us with a wealth of information about both known and unknown degrees of freedom in the standard model. To harness this information, however, the galaxy bias has to be determined in the presence of massive relics, which is expected to vary with scale. Here we present the code RelicFast, which efficiently computes the scale-dependent bias induced by relics of different masses, spins, and temperatures, through spherical collapse and the peak-background split. Using this code, we find that, in general, the bias induced by light relics partially compensates the suppression of power, and should be accounted for in any search for relics with galaxy data. In particular, for the case of neutrinos, we find that both the normal and inverted hierarchies present a percent-level step in the Lagrangian bias, with a size scaling linearly with the neutrino-mass sum, in agreement with recent simulations. RelicFast can compute halo bias in under a second, allowing for this effect to be properly included for different cosmologies, and light relics, at little computational cost.
52 citations
TL;DR: In this article, the bias is a multivariate function of halo properties that falls into three regimes: early-forming, low-mass and late-forming haloes, and the bias depends sensitively on the recent mass accretion history.
Abstract: We develop a novel approach in exploring the joint dependence of halo bias on multiple halo properties using Gaussian process regression. Using a $\Lambda$CDM $N$-body simulation, we carry out a comprehensive study of the joint bias dependence on halo structure, formation history and environment. We show that the bias is a multivariate function of halo properties that falls into three regimes. For massive haloes, halo mass explains the majority of bias variation. For early-forming haloes, bias depends sensitively on the recent mass accretion history. For low-mass and late-forming haloes, bias depends more on the structure of a halo such as its shape and spin. Our framework enables us to convincingly prove that $V_\mathrm{max}/V_\mathrm{vir}$ is a lossy proxy of formation time for bias modelling, whereas the mass, spin, shape and formation time variables are non-redundant with respect to each other. Combining mass and formation time largely accounts for the mass accretion history dependence of bias. Combining all the internal halo properties fully accounts for the density profile dependence inside haloes, and predicts the clustering variation of individual haloes to a $20\%$ level at $\sim 10\mathrm{Mpc}h^{-1}$. When an environmental density is measured outside $1\mathrm{Mpc}h^{-1}$ from the halo centre, it outperforms and largely accounts for the bias dependence on the internal halo structure, explaining the bias variation above a level of $30\%$.
52 citations
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...…Ωm = 0.276, baryonic matter density Ωb = 0.045, Hubble constant H0 = 100h kms −1Mpc−1 with h = 0.7, primordial scalar spectral index ns = 0.961 and r.m.s. linear fluctuations in spheres of radius 8h−1Mpc, σ8 = 0.811, with a transfer function generated by the code camb (Lewis et al. 2000).2...
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