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Register a new userJoint constraints on galaxy bias and $sigma_8$ through the N-pdf of the galaxy number density
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We present a full description of the N-probability density function of the galaxy number density fluctuations. This N-pdf is given in terms, on the one hand, of the cold dark matter correlations and, on the other hand, of the galaxy bias parameter. The method relies on the assumption commonly adopted that the dark matter density fluctuations follow a local non-linear transformation of the initial energy density perturbations. The N-pdf of the galaxy number density fluctuations allows for an optimal estimation of the bias parameter (e.g., via maximum-likelihood estimation, or Bayesian inference if there exists any a priori information on the bias parameter), and of those parameters defining the dark matter correlations, in particular its amplitude ($sigma_8$). It also provides the proper framework to perform model selection between two competitive hypotheses. The parameters estimation capabilities of the N-pdf are proved by SDSS-like simulations (both ideal log-normal simulations and mocks obtained from Las Damas simulations), showing that our estimator is unbiased. We apply our formalism to the 7th release of the SDSS main sample (for a volume-limited subset with absolute magnitudes $M_r leq -20$). We obtain $hat{b} = 1.193 pm 0.074$ and $hat{sigma_8} = 0.862 pm 0.080$, for galaxy number density fluctuations in cells of a size of $30h^{-1}$Mpc. Different model selection criteria show that galaxy biasing is clearly favoured.
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We compare predictions for galaxy-galaxy lensing profiles and clustering from the Henriques et al. (2015) public version of the Munich semi-analytical model of galaxy formation (SAM) and the IllustrisTNG suite, primarily TNG300, with observations from KiDS+GAMA and SDSS-DR7 using four different selection functions for the lenses (stellar mass, stellar mass and group membership, stellar mass and isolation criteria, stellar mass and colour). We find that this version of the SAM does not agree well with the current data for stellar mass-only lenses with $M_ast > 10^{11},M_odot$. By decreasing the merger time for satellite galaxies as well as reducing the radio-mode AGN accretion efficiency in the SAM, we obtain better agreement, both for the lensing and the clustering, at the high mass end. We show that the new model is consistent with the signals for central galaxies presented in Velliscig et al. (2017). Turning to the hydrodynamical simulation, TNG300 produces good lensing predictions, both for stellar mass-only ($chi^2 = 1.81$ compared to $chi^2 = 7.79$ for the SAM), and locally brightest galaxies samples ($chi^2 = 3.80$ compared to $chi^2 = 5.01$). With added dust corrections to the colours it matches the SDSS clustering signal well for red low mass galaxies. We find that both the SAMs and TNG300 predict $sim 50,%$ excessive lensing signals for intermediate mass red galaxies with $10.2 < log_{10} M_ast [ M_odot ] < 11.2$ at $r approx 0.6,h^{-1},mathrm{Mpc}$, which require further theoretical development.
If the formation of central galaxies in dark matter haloes traces the assembly history of their host haloes, in haloes of fixed mass, central galaxy clustering may show dependence on properties indicating their formation history. Such a galaxy assembly bias effect has been investigated by Lin et al. 2016, with samples of central galaxies constructed in haloes of similar mass and with mean halo mass verified by galaxy lensing measurements, and no significant evidence of assembly bias is found from the analysis of the projected two-point correlation functions of early- and late-forming central galaxies. In this work, we extend the the investigation of assembly bias effect from real space to redshift (velocity) space, with an extended construction of early- and late-forming galaxies. We carry out halo occupation distribution modelling to constrain the galaxy-halo connection to see whether there is any sign of the effect of assembly bias. We find largely consistent host halo mass for early- and late-forming central galaxies, corroborated by lensing measurements. The central velocity bias parameters, which are supposed to characterise the mutual relaxation between central galaxies and their host haloes, are inferred to overlap between early- and late-forming central galaxies. However, we find a large amplitude of velocity bias for early-forming central galaxies (e.g. with central galaxies moving at more than 50% that of dark matter velocity dispersion inside host haloes), which may signal an assembly bias effect. A large sample with two-point correlation functions and other clustering measurements and improved modelling will help reach a conclusive result.
We study the impact that uncertainties on assumed relations between galaxy bias parameters have on constraints of the local PNG $f_{rm NL}$ parameter. We focus on the relation between the linear density galaxy bias $b_1$ and local PNG bias $b_phi$ in an idealized forecast setup with multitracer galaxy power spectrum and bispectrum data. We consider two parametrizations of galaxy bias: 1) one inspired by the universality relation where $b_phi = 2delta_cleft(b_1 - pright)$ and $p$ is a free parameter; and 2) another in which the product of bias parameters and $f_{rm NL}$, like $f_{rm NL} b_phi$, is directly fitted for. The constraints on the $f_{rm NL}-p$ plane are markedly bimodal, and both the central value and width of marginalized constraints on $f_{rm NL}$ depend sensitively on the priors on $p$. Assuming fixed $p=1$ in the constraints with a fiducial value of $p=0.55$ can bias the inferred $f_{rm NL}$ by $0.5sigma$ to $1sigma$; priors $Delta p approx 0.5$ around this fiducial value are however sufficient in our setup to return unbiased constraints. In power spectrum analyses, parametrization 2, that makes no assumptions on $b_phi$, can distinguish $f_{rm NL} eq 0$ with the same significance as parametrization 1 assuming perfect knowledge of $b_phi$ (the value of $f_{rm NL}$ is however left unknown). A drawback of parametrization 2 is that the addition of the bispectrum information is not as beneficial as in parametrization 1. Our results motivate strongly the incorporation of mitigation strategies for bias uncertainties in PNG constraint analyses, as well as further theoretical studies on the relations between bias parameters to better inform those strategies.
We generate mock galaxy catalogues for a grid of different cosmologies, using rescaled N-body simulations in tandem with a semi-analytic model run using consistent parameters. Because we predict the galaxy bias, rather than fitting it as a nuisance parameter, we obtain an almost pure constraint on sigma_8 by comparing the projected two-point correlation function we obtain to that from the SDSS. A systematic error arises because different semi-analytic modelling assumptions allow us to fit the r-band luminosity function equally well. Combining our estimate of the error from this source with the statistical error, we find sigma_8=0.97 +/- 0.06. We obtain consistent results if we use galaxy samples with a different magnitude threshold, or if we select galaxies by b_J-band rather than r-band luminosity and compare to data from the 2dFGRS. Our estimate for sigma_8 is higher than that obtained for other analyses of galaxy data alone, and we attempt to find the source of this difference. We note that in any case, galaxy clustering data provide a very stringent constraint on galaxy formation models.
We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the latter we assume a wide survey at relatively large redshifts, similar to the planned Euclid survey, as baseline for future experiments. To assess the impact of the bias we perform a Fisher matrix analysis and we adopt two different parametrizations of scale-dependent bias. The fiducial models for galaxy bias are calibrated using a mock catalogs of H$alpha$ emitting galaxies mimicking the expected properties of the objects that will be targeted by the Euclid survey. In our analysis we have obtained two main results. First of all, allowing for a scale-dependent bias does not significantly increase the errors on the other cosmological parameters apart from the rms amplitude of density fluctuations, $sigma_{8}$, and the growth index $gamma$, whose uncertainties increase by a factor up to two, depending on the bias model adopted. Second, we find that the accuracy in the linear bias parameter $b_{0}$ can be estimated to within 1-2% at various redshifts regardless of the fiducial model. The non-linear bias parameters have significantly large errors that depend on the model adopted. Despite of this, in the more realistic scenarios departures from the simple linear bias prescription can be detected with a $sim2,sigma$ significance at each redshift explored. Finally, we use the Fisher Matrix formalism to assess the impact of assuming an incorrect bias model and found that the systematic errors induced on the cosmological parameters are similar or even larger than the statistical ones.
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