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Register a new userReal- and redshift-space halo clustering in $f(R)$ cosmologies
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Pablo Arnalte-Mur
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We present two-point correlation function statistics of the mass and the halos in the chameleon $f(R)$ modified gravity scenario using a series of large volume N-body simulations. Three distinct variations of $f(R)$ are considered (F4, F5 and F6) and compared to a fiducial $Lambda$CDM model in the redshift range $z in [0,1]$. We find that the matter clustering is indistinguishable for all models except for F4, which shows a significantly steeper slope. The ratio of the redshift- to real-space correlation function at scales $> 20 h^{-1} mathrm{Mpc}$ agrees with the linear General Relativity (GR) Kaiser formula for the viable $f(R)$ models considered. We consider three halo populations characterized by spatial abundances comparable to that of luminous red galaxies (LRGs) and galaxy clusters. The redshift-space halo correlation functions of F4 and F5 deviate significantly from $Lambda$CDM at intermediate and high redshift, as the $f(R)$ halo bias is smaller or equal to that of the $Lambda$CDM case. Finally we introduce a new model independent clustering statistic to distinguish $f(R)$ from GR: the relative halo clustering ratio -- $mathcal{R}$. The sampling required to adequately reduce the scatter in $mathcal{R}$ will be available with the advent of the next generation galaxy redshift surveys. This will foster a prospective avenue to obtain largely model-independent cosmological constraints on this class of modified gravity models.
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The mapping of galaxy clustering from real space to redshift space introduces the anisotropic property to the measured galaxy density power spectrum in redshift space, known as the redshift space distortion (RSD) effect. The mapping formula is intrinsically non-linear, which is complicated by the higher order polynomials due to indefinite orders of cross correlations between density and velocity fields, and the Finger--of--God (FoG) effect due to the randomness of the galaxy peculiar velocity field. In previous works, we have verified the robustness of advanced TNS mapping formula in our hybrid RSD model in dark matter case, where the halo bias models are not taken into account for the halo mapping formula in redshift space. Using 100 realizations of halo catalogs in N-body simulations, we find that our halo RSD model with the known halo bias model and the effective FoG function accurately predicts the halo power spectrum measurements, within 1$sim$2% accuracy up to $ksim 0.2h$/Mpc, depending on different halo masses and redshifts.
We use large volume N-body simulations to predict the clustering of dark matter in redshift space in f(R) modified gravity cosmologies. This is the first time that the nonlinear matter and velocity fields have been resolved to such a high level of accuracy over a broad range of scales in this class of models. We find significant deviations from the clustering signal in standard gravity, with an enhanced boost in power on large scales and stronger damping on small scales in the f(R) models compared to GR at redshifts z<1. We measure the velocity divergence (P_theta theta) and matter (P_delta delta) power spectra and find a large deviation in the ratios sqrt{P_theta theta/P_delta delta} and P_delta theta/P_deltadelta, between the f(R) models and GR for 0.03<k/(h/Mpc)<0.5. In linear theory these ratios equal the growth rate of structure on large scales. Our results show that the simulated ratios agree with the growth rate for each cosmology (which is scale dependent in the case of modified gravity) only for extremely large scales, k<0.06h/Mpc at z=0. The velocity power spectrum is substantially different in the f(R) models compared to GR, suggesting that this observable is a sensitive probe of modified gravity. We demonstrate how to extract the matter and velocity power spectra from the 2D redshift space power spectrum, P(k,mu), and can recover the nonlinear matter power spectrum to within a few percent for k<0.1h/Mpc. However, the model fails to describe the shape of the 2D power spectrum demonstrating that an improved model is necessary in order to reconstruct the velocity power spectrum accurately. The same model can match the monopole moment to within 3% for GR and 10% for the f(R) cosmology at k<0.2 h/Mpc at z=1. Our results suggest that the extraction of the velocity power spectrum from future galaxy surveys is a promising method to constrain deviations from GR.
Modifications of the equations of general relativity at large distances offer one possibility to explain the observed properties of our Universe without invoking a cosmological constant. Numerous proposals for such modified gravity cosmologies exist, but often their consequences for structure formation in the non-linear sector are not yet accurately known. In this work, we employ high-resolution numerical simulations of f(R)-gravity models coupled with a semi-analytic model (SAM) for galaxy formation to obtain detailed predictions for the evolution of galaxy properties. The f(R)-gravity models imply the existence of a `fifth-force, which is however locally suppressed, preserving the successes of general relativity on solar system scales. We show that dark matter haloes in f(R)-gravity models are characterized by a modified virial scaling with respect to the LCDM scenario, reflecting a higher dark matter velocity dispersion at a given mass. This effect is taken into account in the SAM by an appropriate modification of the mass--temperature relation. We find that the statistical properties predicted for galaxies (such as the stellar mass function and the cosmic star formation rate) in f(R)-gravity show generally only very small differences relative to LCDM, smaller than the dispersion between the results of different SAM models, which can be viewed as a measure of their systematic uncertainty. We also demonstrate that galaxy bias is not able to disentangle between f(R)-gravity and the standard cosmological scenario. However, f(R)-gravity imprints modifications in the linear growth rate of cosmic structures at large scale, which can be recovered from the statistical properties of large galaxy samples.
The statistical properties of dark matter halos, the building blocks of cosmological observables associated with structure in the universe, offer many opportunities to test models for cosmic acceleration, especially those that seek to modify gravitational forces. We study the abundance, bias and profiles of halos in cosmological simulations for one such model: the modified action f(R) theory. In the large field regime that is accessible to current observations, enhanced gravitational forces raise the abundance of rare massive halos and decrease their bias but leave their (lensing) mass profiles largely unchanged. This regime is well described by scaling relations based on a modification of spherical collapse calculations. In the small field regime, enhanced forces are suppressed inside halos and the effects on halo properties are substantially reduced for the most massive halos. Nonetheless, the scaling relations still retain limited applicability for the purpose of establishing conservative upper limits on the modification to gravity.
The use of photometric redshifts in cosmology is increasing. Often, however these photo-zs are treated like spectroscopic observations, in that the peak of the photometric redshift, rather than the full probability density function (PDF), is used. This overlooks useful information inherent in the full PDF. We introduce a new real-space estimator for one of the most used cosmological statistics, the 2-point correlation function, that weights by the PDF of individual photometric objects in a manner that is optimal when Poisson statistics dominate. As our estimator does not bin based on the PDF peak it substantially enhances the clustering signal by usefully incorporating information from all photometric objects that overlap the redshift bin of interest. As a real-world application, we measure QSO clustering in the Sloan Digital Sky Survey (SDSS). We find that our simplest binned estimator improves the clustering signal by a factor equivalent to increasing the survey size by a factor of 2-3. We also introduce a new implementation that fully weights between pairs of objects in constructing the cross-correlation and find that this pair-weighted estimator improves clustering signal in a manner equivalent to increasing the survey size by a factor of 4-5. Our technique uses spectroscopic data to anchor the distance scale and it will be particularly useful where spectroscopic data (e.g, from BOSS) overlaps deeper photometry (e.g.,from Pan-STARRS, DES or the LSST). We additionally provide simple, informative expressions to determine when our estimator will be competitive with the autocorrelation of spectroscopic objects. Although we use QSOs as an example population, our estimator can and should be applied to any clustering estimate that uses photometric objects.
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