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Ravi K. Sheth

Publications and source records attributed to Ravi K. Sheth.

At least 109 records · Page 6Linked to original sources

Formation times, mass growth histories and concentrations of dark matter haloes

We develop a simple model for estimating the mass growth histories of dark matter halos. The model is based on a fit to the formation time distribution, where formation is defined as the earliest time that the main branch of the merger tree contains a fraction f of the final mass M . Our analysis exploits the fact that the median formation time as a function of f is the same as the median of the main progenitor mass distribution as a function of time. When coupled with previous work showing that the concentration c of the final halo is related to the formation time tf associated with f \sim 0.04, our approach provides a simple algorithm for estimating how the distribution of halo concentrations may be expected to depend on mass, redshift and the expansion history of the background cosmology. We also show that one can predict log10 c with a precision of about 0.13 and 0.09 dex if only its mass, or both mass and tf are known. And, conversely, one can predict log10 tf from mass or c with a precision of 0.12 and 0.09 dex, approximately independent of f. Adding the mass to the c-based estimate does not result in further improvement. These latter results may be useful for studies which seek to compare the age of the stars in the central galaxy in a halo with the time the core was first assembled.

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One step beyond: The excursion set approach with correlated steps

We provide a simple formula that accurately approximates the first crossing distribution of barriers having a wide variety of shapes, by random walks with a wide range of correlations between steps. Special cases of it are useful for estimating halo abundances, evolution, and bias, as well as the nonlinear counts in cells distribution. We discuss how it can be extended to allow for the dependence of the barrier on quantities other than overdensity, to construct an excursion set model for peaks, and to show why assembly and scale dependent bias are generic even at the linear level.

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A hierarchy of voids: More ado about nothing

We extend earlier work on the problem of estimating the void-volume function -- the abundance and evolution of large voids which grow gravitationally in an expanding universe -- in two ways. The first removes an ambiguity about how the void-in-cloud process, which erases small voids, should be incorporated into the excursion set approach. The main technical change here is to think of voids within a fully Eulerian, rather than purely Lagrangian, framework. The second accounts for correlations between different spatial scales in the initial conditions. We provide numerical and analytical arguments showing how and why both changes modify the predicted abundances substantially. In particular, we show that the predicted importance of the void-in-cloud process depends strongly on whether or not one accounts for correlations between scales. With our new formulation, the void-in-cloud process dramatically reduces the predicted abundances of voids if such correlations are ignored, but only matters for the smallest voids in the more realistic case in which the spatial correlations are included.

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Halo bias in the excursion set approach with correlated steps

In the Excursion Set approach, halo abundances and clustering are closely related. This relation is exploited in many modern methods which seek to constrain cosmological parameters on the basis of the observed spatial distribution of clusters. However, to obtain analytic expressions for these quantities, most Excursion Set based predictions ignore the fact that, although different k-modes in the initial Gaussian field are uncorrelated, this is not true in real space: the values of the density field at a given spatial position, when smoothed on different real-space scales, are correlated in a nontrivial way. We show that when the excursion set approach is extended to include such correlations, then one must be careful to account for the fact that the associated prediction for halo bias is explicitly a real-space quantity. Therefore, care must be taken when comparing the predictions of this approach with measurements in simulations, which are typically made in Fourier-space. We show how to correct for this effect, and demonstrate that ignorance of this effect in recent analyses of halo bias has led to incorrect conclusions and biased constraints.

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Halo abundances and counts-in-cells: The excursion set approach with correlated steps

The Excursion Set approach has been used to make predictions for a number of interesting quantities in studies of nonlinear hierarchical clustering. These include the halo mass function, halo merger rates, halo formation times and masses, halo clustering, analogous quantities for voids, and the distribution of dark matter counts in randomly placed cells. The approach assumes that all these quantities can be mapped to problems involving the first crossing distribution of a suitably chosen barrier by random walks. Most analytic expressions for these distributions ignore the fact that, although different k-modes in the initial Gaussian field are uncorrelated, this is not true in real space: the values of the density field at a given spatial position, when smoothed on different real-space scales, are correlated in a nontrivial way. As a result, the problem is to estimate first crossing distribution by random walks having correlated rather than uncorrelated steps. In 1990, Peacock & Heavens presented a simple approximation for the first crossing distribution of a single barrier of constant height by walks with correlated steps. We show that their approximation can be thought of as a correction to the distribution associated with what we call smooth completely correlated walks. We then use this insight to extend their approach to treat moving barriers, as well as walks that are constrained to pass through a certain point before crossing the barrier. For the latter, we show that a simple rescaling, inspired by bivariate Gaussian statistics, of the unconditional first crossing distribution, accurately describes the conditional distribution, independently of the choice of analytical prescription for the former. In all cases, comparison with Monte-Carlo solutions of the problem shows reasonably good agreement. (Abridged)

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Modelling the shapes of the largest gravitationally bound objects

We combine the physics of the ellipsoidal collapse model with the excursion set theory to study the shapes of dark matter halos. In particular, we develop an analytic approximation to the nonlinear evolution that is more accurate than the Zeldovich approximation; we introduce a planar representation of halo axis ratios, which allows a concise and intuitive description of the dynamics of collapsing regions and allows one to relate the final shape of a halo to its initial shape; we provide simple physical explanations for some empirical fitting formulae obtained from numerical studies. Comparison with simulations is challenging, as there is no agreement about how to define a non-spherical gravitationally bound object. Nevertheless, we find that our model matches the conditional minor-to-intermediate axis ratio distribution rather well, although it disagrees with the numerical results in reproducing the minor-to-major axis ratio distribution. In particular, the mass dependence of the minor-to-major axis distribution appears to be the opposite to what is found in many previous numerical studies, where low-mass halos are preferentially more spherical than high-mass halos. In our model, the high-mass halos are predicted to be more spherical, consistent with results based on a more recent and elaborate halo finding algorithm, and with observations of the mass dependence of the shapes of early-type galaxies. We suggest that some of the disagreement with some previous numerical studies may be alleviated if we consider only isolated halos.

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Scale-dependent halo bias from scale-dependent growth

We derive a general expression for the large-scale halo bias, in theories with a scale-dependent linear growth, using the excursion set formalism. Such theories include modified gravity models, and models in which the dark energy clustering is non-negligible. A scale dependence is imprinted in both the formation and evolved biases by the scale-dependent growth. Mergers are accounted for in our derivation, which thus extends earlier work which focused on passive evolution. There is a simple analytic form for the bias for those theories in which the nonlinear collapse of perturbations is approximately the same as in general relativity. As an illustration, we apply our results to a simple Yukawa modification of gravity, and use SDSS measurements of the clustering of luminous red galaxies to constrain the theory's parameters.

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Optimal linear reconstruction of dark matter from halo catalogs

We derive the weight function w(M) to apply to dark-matter halos that minimizes the stochasticity between the weighted halo distribution and its underlying mass density field. The optimal w(M) depends on the range of masses being used in the estimator. In N-body simulations, the Poisson estimator is up to 15 times noisier than the optimal. Implementation of the optimal weight yields significantly lower stochasticity than weighting halos by their mass, bias or equal. Optimal weighting could make cosmological tests based on the matter power spectrum or cross-correlations much more powerful and/or cost-effective. A volume-limited measurement of the mass power spectrum at k=0.2h/Mpc over the entire z<1 universe could ideally be done using only 6 million redshifts of halos with mass M>6\times10^{13}h^{-1}M_\odot (1\times10^{13}) at z=0 (z=1); this is 5 times fewer than the Poisson model predicts. Using halo occupancy distributions (HOD) we find that uniformly-weighted catalogs of luminous red galaxies require >3 times more redshifts than an optimally-weighted halo catalog to reconstruct the mass to the same accuracy. While the mean HODs of galaxies above a threshold luminosity are similar to the optimal w(M), the stochasticity of the halo occupation degrades the mass estimator. Blue or emission-line galaxies are about 100 times less efficient at reconstructing mass than an optimal weighting scheme. This suggests an efficient observational approach of identifying and weighting halos with a deep photo-z survey before conducting a spectroscopic survey. The optimal w(M) and mass-estimator stochasticity predicted by the standard halo model for M>10^{12}h^{-1}M_\odot are in reasonable agreement with our measurements, with the important exceptions that the halos must be assumed to be linearly biased samples of a "halo field" that is distinct from the mass field. (Abridged)

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Modeling scale-dependent bias on the baryonic acoustic scale with the statistics of peaks of Gaussian random fields

Models of galaxy and halo clustering commonly assume that the tracers can be treated as a continuous field locally biased with respect to the underlying mass distribution. In the peak model pioneered by BBKS, one considers instead density maxima of the initial, Gaussian mass density field as an approximation to the formation site of virialized objects. In this paper, the peak model is extended in two ways to improve its predictive accuracy. Firstly, we derive the two-point correlation function of initial density peaks up to second order and demonstrate that a peak-background split approach can be applied to obtain the k-independent and k-dependent peak bias factors at all orders. Secondly, we explore the gravitational evolution of the peak correlation function within the Zel'dovich approximation. We show that the local (Lagrangian) bias approach emerges as a special case of the peak model, in which all bias parameters are scale-independent and there is no statistical velocity bias. We apply our formulae to study how the Lagrangian peak biasing, the diffusion due to large scale flows and the mode-coupling due to nonlocal interactions affect the scale dependence of bias from small separations up to the baryon acoustic oscillation (BAO) scale. For 2-sigma density peaks collapsing at z=0.3, our model predicts a ~ 5% residual scale-dependent bias around the acoustic scale that arises mostly from first-order Lagrangian peak biasing (as opposed to second-order gravity mode-coupling). We also search for a scale dependence of bias in the large scale auto-correlation of massive halos extracted from a very large N-body simulation provided by the MICE collaboration. For halos with mass M>10^{14}Msun/h, our measurements demonstrate a scale-dependent bias across the BAO feature which is very well reproduced by a prediction based on the peak model.

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The nonlinear redshift space probability distribution function in models with local primordial non-Gaussianity

We use the ellipsoidal collapse approximation to investigate the nonlinear redshift space evolution of the density field with primordial non-Gaussianity of the local f_{nl}-type. We utilize the joint distribution of eigenvalues of the initial non-Gaussian shear field and evaluate the evolved redshift space probability distribution function (PDF). It is shown that, similar to the real space analysis, the underdense tail of the nonlinear redshift space PDF differs significantly from that for Gaussian initial conditions. We also derive the lowest order correction of the Kaiser's formulain the presence of a non-zero f_{nl}.

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Halo model description of the non-linear dark matter power spectrum at $k \gg 1$ Mpc$^{-1}$

Accurate knowledge of the non-linear dark-matter power spectrum is important for understanding the large-scale structure of the Universe, the statistics of dark-matter haloes and their evolution, and cosmological gravitational lensing. We analytically model the dark-matter power spectrum and its cross-power spectrum with dark-matter haloes. Our model extends the halo-model formalism, including realistic substructure population within individual dark-matter haloes and the scatter of the concentration parameter at fixed halo mass. We consider three prescriptions for the mass-concentration relation and two for the substructure distribution in dark-matter haloes. We show that this extension of the halo model mainly increases the predicted power on the small scales, and is crucial for proper modeling the cosmological weak-lensing signal due to low-mass haloes. Our extended formalism shows how the halo model approach can be improved in accuracy as one increases the number of ingredients that are calibrated from n-body simulations.

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Redshift space correlations and scale-dependent stochastic biasing of density peaks

We calculate the redshift space correlation function and the power spectrum of density peaks of a Gaussian random field. In the linear regime k < 0.1 h/Mpc, the redshift space power spectrum is P^s_{pk}(k,u) = exp(-f^2 s_{vel}^2 k^2 u^2) * [b_{pk}(k) + b_{vel}(k) f u^2]^2 * P_m(k), where u is the angle with respect to the line of sight, s_{vel} is the one-dimensional velocity dispersion, f is the growth rate, and b_{pk}(k) and b_{vel}(k) are k-dependent linear spatial and velocity bias factors. For peaks, the value of s_{vel} depends upon the functional form of b_{vel}. The peaks model is remarkable because it has unbiased velocities -- peak motions are driven by dark matter flows -- but, in order to achieve this, b_{vel} is k-dependent. We speculate that this is true in general: k-dependence of the spatial bias will lead to k-dependence of b_{vel} even if the biased tracers flow with the dark matter. Because of the k-dependence of the linear bias parameters, standard manipulations applied to the peak model will lead to k-dependent estimates of the growth factor that could erroneously be interpreted as a signature of modified dark energy or gravity. We use the Fisher formalism to show that the constraint on the growth rate f is degraded by a factor of two if one allows for a k-dependent velocity bias of the peak type. We discuss a simple estimate of nonlinear evolution and illustrate the effect of the peak bias on the redshift space multipoles. For k < 0.1 h/Mpc, the peak bias is deterministic but k-dependent, so the configuration space bias is stochastic and scale dependent, both in real and redshift space. We provide expressions for this stochasticity and its evolution (abridged).

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The Substructure Hierarchy in Dark Matter Haloes

We present a new algorithm for identifying the substructure within simulated dark matter haloes. The method is an extension of that proposed by Tormen et al. (2004) and Giocoli et al. (2008a), which identifies a subhalo as a group of self-bound particles that prior to being accreted by the main progenitor of the host halo belonged to one and the same progenitor halo (hereafter satellite). However, this definition does not account for the fact that these satellite haloes themselves may also have substructure, which thus gives rise to sub-subhaloes, etc. Our new algorithm identifies substructures at all levels of this hierarchy, and we use it to determine the mass function of all substructure (counting sub-haloes, sub-subhaloes, etc.). On average, haloes which formed more recently tend to have a larger mass fraction in substructure and to be less concentrated than average haloes of the same mass. We provide quantitative fits to these correlations. Even though our algorithm is very different from that of Gao et al. (2004), we too find that the subhalo mass function per unit mass at redshift z = 0 is universal. This universality extends to any redshift only if one accounts for the fact that host haloes of a given mass are less concentrated at higher redshifts, and concentration and substructure abundance are anti-correlated. This universality allows a simple parametrization of the subhalo mass function integrated over all host halo masses, at any given time. We provide analytic fits to this function which should be useful in halo model analyses which equate galaxies with halo substructure when interpreting clustering in large sky surveys. Finally, we discuss systematic differences in the subhalo mass function that arise from different definitions of (host) halo mass.

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Convolution and deconvolution based estimates of galaxy scaling relations from photometric redshift surveys

In addition to the maximum likelihood approach, there are two other methods which are commonly used to reconstruct the true redshift distribution from photometric redshift datasets: one uses a deconvolution method, and the other a convolution. We show how these two techniques are related, and how this relationship can be extended to include the study of galaxy scaling relations in photometric datasets. We then show what additional information photometric redshift algorithms must output so that they too can be used to study galaxy scaling relations, rather than just redshift distributions. We also argue that the convolution based approach may permit a more efficient selection of the objects for which calibration spectra are required.

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Sizes and ages of SDSS ellipticals: Comparison with hierarchical galaxy formation models

In a sample of about 45,700 early-type galaxies extracted from SDSS, we find that the shape, normalization, and dispersion around the mean size-stellar mass relation is the same for young and old systems, provided the stellar mass is greater than 3*10^10 Msun. This is difficult to reproduce in pure passive evolution models, which generically predict older galaxies to be much more compact than younger ones of the same stellar mass. However, this aspect of our measurements is well reproduced by hierarchical models of galaxy formation. Whereas the models predict more compact galaxies at high redshifts, subsequent minor, dry mergers increase the sizes of the more massive objects, resulting in a flat size-age relation at the present time. At lower masses, the models predict that mergers are less frequent, so that the expected anti-correlation between age and size is not completely erased. This is in good agreement with our data: below 3*10^10 Msun, the effective radius R_e is a factor of ~2 lower for older galaxies. These successes of the models are offset by the fact that the predicted sizes have other serious problems, which we discuss.

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Density profiles and voids in modified gravity models

We study the formation of voids in a modified gravity model in which gravity is generically stronger or weaker on large scales. We show that void abundances provide complementary information to halo abundances: if normalized to the CMB, models with weaker large-scale gravity have smaller large scale power, fewer massive halos and fewer large voids, although the scalings are not completely degenerate with $σ_8$. Our results suggest that, in addition to their abundances, halo and void density profiles may also provide interesting constraints on such models: stronger large scale gravity produces more concentrated halos, and thinner void walls. This potentially affects the scaling relations commonly assumed to translate cluster observables to halo masses, potentially making these too, useful probes of gravity.

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Reconstructing galaxy fundamental distributions and scaling relations from photometric redshift surveys. Applications to the SDSS early-type sample

Noisy distance estimates associated with photometric rather than spectroscopic redshifts lead to a mis-estimate of the luminosities, and produce a correlated mis-estimate of the sizes. We consider a sample of early-type galaxies from the SDSS DR6 for which both spectroscopic and photometric information is available, and apply the generalization of the V_max method to correct for these biases. We show that our technique recovers the true redshift, magnitude and size distributions, as well as the true size-luminosity relation. We find that using only 10% of the spectroscopic information randomly spaced in our catalog is sufficient for the reconstructions to be accurate within about 3%, when the photometric redshift error is dz = 0.038. We then address the problem of extending our method to deep redshift catalogs, where only photometric information is available. In addition to the specific applications outlined here, our technique impacts a broader range of studies, when at least one distance-dependent quantity is involved. It is particularly relevant for the next generation of surveys, some of which will only have photometric information.

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Halo abundances in the f_{nl} model

We show how the excursion set moving barrier model for halo abundances may be generalized to the local non-Gaussian f_{nl} model. Our estimate assumes that the distribution of step sizes depends on f_{nl}, but that they are otherwise uncorrelated. Our analysis is consistent with previous results for the case of a constant barrier, and highlights some implicit assumptions. It also clarifies the basis of an approximate analytic solution to the moving barrier problem in the Gaussian case, and shows how it might be improved.

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