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Francisco-Shu Kitaura

Publications and source records attributed to Francisco-Shu Kitaura.

At least 91 records · Page 5Linked to original sources

SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large scale structure catalogues

The Baryon Oscillation Spectroscopic Survey (BOSS), part of the Sloan Digital Sky Survey (SDSS) III project, has provided the largest survey of galaxy redshifts available to date, in terms of both the number of galaxy redshifts measured by a single survey, and the effective cosmological volume covered. Key to analysing the clustering of these data to provide cosmological measurements is understanding the detailed properties of this sample. Potential issues include variations in the target catalogue caused by changes either in the targeting algorithm or properties of the data used, the pattern of spectroscopic observations, the spatial distribution of targets for which redshifts were not obtained, and variations in the target sky density due to observational systematics. We document here the target selection algorithms used to create the galaxy samples that comprise BOSS. We also present the algorithms used to create large scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask. The algorithms are an evolution of those used by the BOSS team to construct catalogues from earlier data, and have been designed to accurately quantify the galaxy sample. The code used, designated MKSAMPLE, is released with this paper.

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Bayesian redshift-space distortions correction from galaxy redshift surveys

We present a Bayesian reconstruction method which maps a galaxy distribution from redshift-space to real-space inferring the distances of the individual galaxies. The method is based on sampling density fields assuming a lognormal prior with a likelihood given by the negative binomial distribution function modelling stochastic bias. We assume a deterministic bias given by a power law relating the dark matter density field to the expected halo or galaxy field. Coherent redshift-space distortions are corrected in a Gibbs-sampling procedure by moving the galaxies from redshift-space to real-space according to the peculiar motions derived from the recovered density field using linear theory with the option to include tidal field corrections from second order Lagrangian perturbation theory. The virialised distortions are corrected by sampling candidate real-space positions (being in the neighbourhood of the observations along the line of sight), which are compatible with the bulk flow corrected redshift-space position adding a random dispersion term in high density collapsed regions. The latter are defined according to the eigenvalues of the Hessian. This approach presents an alternative method to estimate the distances to galaxies using the three dimensional spatial information, and assuming isotropy. Hence the number of applications is very broad. In this work we show the potential of this method to constrain the growth rate up to $k$ ~ 0.3 $h$ Mpc$^{-1}$. Furthermore it could be useful to correct for photo-metric redshift errors, and to obtain improved BAO reconstructions.

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nIFTy Cosmology: Galaxy/halo mock catalogue comparison project on clustering statistics

We present a comparison of major methodologies of fast generating mock halo or galaxy catalogues. The comparison is done for two-point and the three-point clustering statistics. The reference catalogues are drawn from the BigMultiDark N-body simulation. Both friend-of-friends (including distinct halos only) and spherical overdensity (including distinct halos and subhalos) catalogs have been used with the typical number density of a large-volume galaxy surveys. We demonstrate that a proper biasing model is essential for reproducing the power spectrum at quasilinear and even smaller scales. With respect to various clustering statistics a methodology based on perturbation theory and a realistic biasing model leads to very good agreement with N-body simulations. However, for the quadrupole of the correlation function or the power spectrum, only the method based on semi-N-body simulation could reach high accuracy (1% level) at small scales, i.e., r<25 Mpc/h or k>0.15 h/Mpc. Full N-body solutions will remain indispensable to produce reference catalogues. Nevertheless, we have demonstrated that the far more efficient approximate solvers can reach a few percent accuracy in terms of clustering statistics at the scales interesting for the large-scale structure analysis after calibration with a few reference N-body calculations. This makes them useful for massive production aimed at covariance studies, to scan large parameter spaces, and to estimate uncertainties in data analysis techniques, such as baryon acoustic oscillation reconstruction, redshift distortion measurements, etc.

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Constraining the baryon fraction in the Warm Hot Intergalactic Medium at low redshifts with PLANCK data

We cross-correlate foreground cleaned Planck Nominal Cosmic Microwave Background (CMB) maps with two templates constructed from the Two-Micron All-Sky Redshift Survey of galaxies. The first template traces the large-scale filamentary distribution characteristic of the Warm-Hot Intergalactic Medium (WHIM) out to ~90 Mpc/h. The second traces preferentially the virialized gas in unresolved halos around galaxies. We find a marginal signal from the correlation of Planck data and the WHIM template with a signal-to-noise from 0.84 to 1.39 at the different Planck frequencies, and with a frequency dependence compatible with the thermal Sunyaev-Zel'dovich (tSZ) effect. When we restrict our analysis to the 60% of the sky outside the plane of the Galaxy and known point sources and galaxy clusters, the cross-correlation at zero lag is 0.064 +/- 0.051 muK. The correlation extends out to ~6 deg, which at the median depth of our template corresponds to a physical length of ~ 6-8 Mpc/h. On the same fraction of the sky, the cross-correlation of the CMB data with the second template is <0.17 muK (95% C.L.), providing no statistically significant evidence of a contribution from bound gas to the previous result. This limit translates into a physical constraint on the properties of the shock-heated WHIM of a log-normal model describing the weakly nonlinear density field. We find that our upper limit is compatible with a fraction of 45% of all baryons residing in filaments at overdensities ~1-100 and with temperatures in the range 10^4.5-10^7.5 K, in agreement with the detection at redshift z~0.5 of van Waerbeke et al. (2014).

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Halo mass distribution reconstruction across the cosmic web

We study the relation between halo mass and its environment from a probabilistic perspective. We find that halo mass depends not only on local dark matter density, but also on non-local quantities such as the cosmic web environment and the halo-exclusion effect. Given these accurate relations, we have developed the HADRON-code (Halo mAss Distribution ReconstructiON), a technique which permits us to assign halo masses to a distribution of haloes in three-dimensional space. This can be applied to the fast production of mock galaxy catalogues, by assigning halo masses, and reproducing accurately the bias for different mass cuts. The resulting clustering of the halo populations agree well with that drawn from the BigMultiDark $N$-body simulation: the power spectra are within 1-$σ$ up to scales of $k=0.2\,h\,{\rm Mpc}^{-1}$, when using augmented Lagrangian perturbation theory based mock catalogues. Only the most massive haloes show a larger deviation. For these, we find evidence of the halo-exclusion effect. A clear improvement is achieved when assigning the highest masses to haloes with a minimum distance separation. We also compute the 2- and 3-point correlation functions, and find an excellent agreement with $N$-body results. Our work represents a quantitative application of the cosmic web classification. It can have further interesting applications in the multi-tracer analysis of the large-scale structure for future galaxy surveys.

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Hunting down systematics in baryon acoustic oscillations after cosmic high noon

Future dark energy experiments will require better and more accurate theoretical predictions for the baryonic acoustic oscillations (BAO) signature in the spectrum of cosmological perturbations. Here, we use large N-body simulations of the \LambdaCDM Planck cosmology to study any possible systematic shifts and damping in BAO due to the impact of nonlinear gravitational growth of structure, scale dependent and non-local bias, and redshift-space distortions. The effect of cosmic variance is largely reduced by dividing the tracer power spectrum by that from a BAO-free simulation starting with the same phases. This permits us to study with unprecedented accuracy (better than 0.02% for dark matter and 0.07% for low-bias halos) small shifts of the pristine BAO wavenumbers towards larger k, and non-linear damping of BAO wiggles in the power spectrum of dark matter and halo populations in the redshift range z=0-1. For dark matter, we provide an accurate parametrization of the evolution of αas a function of the linear growth factor D(z). For halo samples, with bias ranging from 1.2 to 2.8, we measure a typical BAO shift of ~0.25%, observed in real-space, which does not show an appreciable evolution with redshift within the uncertainties. Moreover, we report a constant shift as a function of halo bias. We find a different evolution of the damping of the acoustic feature in all halo samples as compared to dark matter with haloes suffering less damping, and also find some weak dependence on bias. A larger BAO shift and damping is measured in redshift-space which can be well explained by linear theory due to redshift-space distortions. A clear modulation in phase with the acoustic scale is observed in the scale-dependent halo bias due to the presence of the baryonic acoustic oscillations.

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Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers

We present a Bayesian reconstruction algorithm to generate unbiased samples of the underlying dark matter field from halo catalogues. Our new contribution consists of implementing a non-Poisson likelihood including a deterministic non-linear and scale-dependent bias. In particular we present the Hamiltonian equations of motions for the negative binomial (NB) probability distribution function. This permits us to efficiently sample the posterior distribution function of density fields given a sample of galaxies using the Hamiltonian Monte Carlo technique implemented in the Argo code. We have tested our algorithm with the Bolshoi $N$-body simulation at redshift $z = 0$, inferring the underlying dark matter density field from sub-samples of the halo catalogue with biases smaller and larger than one. Our method shows that we can draw closely unbiased samples (compatible within 1-$σ$) from the posterior distribution up to scales of about $k$~1 h/Mpc in terms of power-spectra and cell-to-cell correlations. We find that a Poisson likelihood yields reconstructions with power spectra deviating more than 10% at $k$=0.2 h/Mpc. Our reconstruction algorithm is especially suited for emission line galaxy data for which a complex non-linear stochastic biasing treatment beyond Poissonity becomes indispensable.

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The cosmic web of the Local Universe: cosmic variance, matter content and its relation to galaxy morphology

We present, for the first time, a Local Universe (LU) characterization using high precision constrained $N$-body simulations based on self-consistent phase-space reconstructions of the large-scale structure in the Two-Micron All-Sky Galaxy Redshift Survey. We analyse whether we live in a special cosmic web environment by estimating cosmic variance from a set of unconstrained $Λ$CDM simulations as a function of distance to random observers. By computing volume and mass filling fractions for voids, sheets, filaments and knots, we find that the LU displays a typical scatter of about $1σ$ at scales $r>15\,h^{-1}\,{\rm Mpc}$, in agreement with $Λ$CDM, converging to a fair unbiased sample when considering spheres of about $60\,h^{-1}\,{\rm Mpc}$ radius. Additionally, we compute the matter density profile of the LU and found a reasonable agreement with the estimates of Karachentsev (2012) only when considering the contribution of dark haloes. This indicates that observational estimates may be biased towards low density values. As a first application of our reconstruction, we investigate the likelihood of different galaxy morphological types to inhabit certain cosmic web environments. In particular, we find that, irrespective of the method used to define the web, either based on the density or the peculiar velocity field, elliptical galaxies show a clear tendency to preferentially reside in clusters as opposed to voids (up to a level of $5.3σ$ and $9.8σ$ respectively) and conversely for spiral galaxies (up to a level of $5.6σ$ and $5.4σ$ respectively). These findings are compatible with previous works, albeit at higher confidence levels.

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Constraining the halo bispectrum in real and redshift space from perturbation theory and non-linear stochastic bias

We present a method to produce mock galaxy catalogues with efficient perturbation theory schemes, which match the number density, power spectra and bispectra in real and in redshift space from N-body simulations. The essential contribution of this work is the way in which we constrain the bias parameters in the PATCHY-code. In addition of aiming at reproducing the two-point statistics, we seek the set of bias parameters, which constrain the univariate halo probability distribution function (PDF) encoding higher-order correlation functions. We demonstrate that halo catalogues based on the same underlying dark matter field with a fix halo number density, and accurately matching the power spectrum (within 2%), can lead to very different bispectra depending on the adopted halo bias model. A model ignoring the shape of the halo PDF can lead to deviations up to factors of 2. The catalogues obtained additionally constraining the shape of the halo PDF can significantly lower the discrepancy in the three-point statistics, yielding closely unbiased bispectra both in real and in redshift space; which are in general compatible with those corresponding to an N-body simulation within 10% (deviating at most up to 20%). Our calculations show that the constant linear bias of ~2 for Luminous Red Galaxy (LRG) like galaxies seen in the power spectrum, mainly comes from sampling halos in high density peaks, choosing a high density threshold rather than from a factor multiplying the dark matter density field. Our method contributes towards an efficient modelling of the halo/galaxy distribution required to estimate uncertainties in the clustering measurements from galaxy redshift surveys. We have also demonstrated that it represents a powerful tool to test various bias models.

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Modelling Baryon Acoustic Oscillations with Perturbation Theory and Stochastic Halo Biasing

In this work we investigate the generation of mock halo catalogues based on perturbation theory and nonlinear stochastic biasing with the novel PATCHY-code. In particular, we use Augmented Lagrangian Perturbation Theory (ALPT) to generate a dark matter density field on a mesh starting from Gaussian fluctuations and to compute the peculiar velocity field. ALPT is based on a combination of second order LPT (2LPT) on large scales and the spherical collapse model on smaller scales. We account for the systematic deviation of perturbative approaches from N-body simulations together with halo biasing adopting an exponential bias model. We then account for stochastic biasing by defining three regimes: a low, an intermediate and a high density regime, using a Poisson distribution in the intermediate regime and the negative binomial distribution to model over-dispersion in the high density regime. Since we focus in this study on massive halos, we suppress the generation of halos in the low density regime. The various nonlinear and stochastic biasing parameters, and density thresholds (five) are calibrated with the large BigMultiDark N-body simulation to match the power spectrum of the corresponding halo population. Our mock catalogues show power spectra, both in real- and redshift-space, which are compatible with N-body simulations within about 2% up to k ~ 1 h Mpc^-1 at z = 0.577 for a sample of halos with the typical BOSS CMASS galaxy number density. The corresponding correlation functions are compatible down to a few Mpc. We also find that neglecting over-dispersion in high density regions produces power spectra with deviations of 10% at k ~ 0.4 h Mpc^-1. These results indicate the need to account for an accurate statistical description of the galaxy clustering for precise studies of large-scale surveys.

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Simulating Structure Formation of the Local Universe

In this work we present cosmological N-body simulations of the Local Universe with initial conditions constrained by the Two-Micron Redshift Survey (2MRS) within a cubic volume of 180 Mpc/h side-length centred at the Local Group. We use a self-consistent Bayesian based approach to explore the joint parameter space of primordial density fluctuations and peculiar velocity fields, which are compatible with the 2MRS galaxy distribution after cosmic evolution. This method (the KIGEN-code) includes the novel ALPT (Augmented Lagrangian Perturbation Theory) structure formation model which combines second order LPT (2LPT) on large scales with the spherical collapse model on small scales. Furthermore we describe coherent flows with 2LPT and include a dispersion term to model fingers-of-god (fogs). These implementations are crucial to avoid artificial filamentary structures, which appear when using a structure formation model with 2LPT and data with compressed fogs. We assume LCDM cosmology throughout our method. The recovered initial Gaussian fields are used to perform a set of 25 constrained simulations. Statistically this ensemble of simulations is in agreement with a reference set of 25 simulations based on randomly seeded Gaussian fluctuations in terms of matter statistics, power-spectra and mass functions. Considering the entire volume, we obtain correlation coefficients of about 98.3% for the comparison between the simulated density fields and the galaxy density field in log-space with Gaussian smoothing scales of r_S=3.5 Mpc/h (74% for r_S=1.4 Mpc/h). The cross power-spectra show correlations with the galaxy distribution which weaken towards smaller length scales until they vanish at scales of 2.2-3.0 Mpc/h. The simulations we present provide a fully nonlinear density and velocity field with a high level of correlation with the observed galaxy distribution at scales of a few Mpc.

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Cosmological Structure Formation with Augmented Lagrangian Perturbation Theory

We present a new fast and efficient approach to model structure formation with Augmented Lagrangian Perturbation Theory (ALPT). Our method is based on splitting the displacement field into a long and a short-range component. The long-range component is computed by second order LPT (2LPT). This approximation contains a tidal nonlocal and nonlinear term. Unfortunately, 2LPT fails on small scales due to severe shell crossing and a crude quadratic behaviour in the low density regime. The spherical collapse (SC) approximation has been recently reported to correct for both effects by adding an ideal collapse truncation. However, this approach fails to reproduce the structures on large scales where it is significantly less correlated with the N-body result than 2LPT or linear LPT (the Zeldovich approximation). We propose to combine both approximations using for the short-range displacement field the SC solution. A Gaussian filter with a smoothing radius r_S is used to separate between both regimes. We use the result of 25 dark matter only N-body simulations to benchmark at z=0 the different approximations: 1st, 2nd, 3rd order LPT, SC and our novel combined ALPT model. This comparison demonstrates that our method improves previous approximations at all scales showing ~25% and ~75% higher correlation than 2LPT with the N-body solution at k = 1 and 2 h Mpc^-1, respectively. We conduct a parameter study to determine the optimal range of smoothing radii and find that the maximum correlation is achieved with r_S = 4 - 5 h^-1 Mpc. This structure formation approach could be used for various purposes, such as setting-up initial conditions for N-body simulations, generating mock galaxy catalogues, cosmic web analysis or for reconstructions of the primordial density fluctuations.

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The Initial Conditions of the Universe from Constrained Simulations

I present a new approach to recover the primordial density fluctuations and the cosmic web structure underlying a galaxy distribution. The method is based on sampling Gaussian fields which are compatible with a galaxy distribution and a structure formation model. This is achieved by splitting the inversion problem into two Gibbs-sampling steps: the first being a Gaussianisation step transforming a distribution of point sources at Lagrangian positions -which are not a priori given- into a linear alias-free Gaussian field. This step is based on Hamiltonian sampling with a Gaussian-Poisson model. The second step consists on a likelihood comparison in which the set of matter tracers at the initial conditions is constrained on the galaxy distribution and the assumed structure formation model. For computational reasons second order Lagrangian Perturbation Theory is used. However, the presented approach is flexible to adopt any structure formation model. A semi-analytic halo-model based galaxy mock catalog is taken to demonstrate that the recovered initial conditions are closely unbiased with respect to the actual ones from the corresponding N-body simulation down to scales of a ~ 5 Mpc/h. The cross-correlation between them shows a substantial gain of information, being at k ~ 0.3 h/Mpc more than doubled. In addition the initial conditions are extremely well Gaussian distributed and the power-spectra follow the shape of the linear power-spectrum being very close to the actual one from the simulation down to scales of k ~ 1 h/Mpc.

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Cosmic Structure and Dynamics of the Local Universe

We present a cosmography analysis of the Local Universe based on the recently released Two-Micron All-Sky Redshift Survey (2MRS). Our method is based on a Bayesian Networks Machine Learning algorithm (the Kigen-code) which self-consistently samples the initial density fluctuations compatible with the observed galaxy distribution and a structure formation model given by second order Lagrangian perturbation theory (2LPT). From the initial conditions we obtain an ensemble of reconstructed density and peculiar velocity fields which characterize the local cosmic structure with high accuracy unveiling nonlinear structures like filaments and voids in detail. Coherent redshift space distortions are consistently corrected within 2LPT. From the ensemble of cross-correlations between the reconstructions and the galaxy field and the variance of the recovered density fields we find that our method is extremely accurate up to k ~ 1 h Mpc^-1 and still yields reliable results down to scales of about 3-4 h^-1 Mpc. The motion of the local group we obtain within ~ 80 h^-1 Mpc (v_LG=522+-86 km s^-1, l_LG=291^o +- 16^o, b_LG=34^o+-8^o) is in good agreement with measurements derived from the CMB and from direct observations of peculiar motions and is consistent with the predictions of LambdaCDM.

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Multiscale Inference of Matter Fields and Baryon Acoustic Oscillations from the Ly-alpha Forest

We present a novel Bayesian method for the joint reconstruction of cosmological matter density fields, peculiar velocities and power-spectra in the quasi-nonlinear regime. We study its applicability to the Ly-alpha forest based on multiple quasar absorption spectra. Our approach to this problem includes a multiscale, nonlinear, two-step scheme since the statistics describing the matter distribution depends on scale, being strongly non-Gaussian on small scales (< 0.1 h^{-1} Mpc) and closely lognormal on scales >~10 h^{-1} Mpc. The first step consists on performing 1D highly resolved matter density reconstructions along the line-of-sight towards z~2-3 quasars based on an arbitrary non-Gaussian univariate model for matter statistics. The second step consists on Gibbs-sampling based on conditional PDFs. The matter density field is sampled in real space with Hamiltonian-sampling using the Poisson/Gamma-lognormal model, while redshift distortions are corrected with linear Lagrangian perturbation theory. The power-spectrum of the lognormal transformed variable which is Gaussian distributed (and thus close to the linear regime) can consistently be sampled with the inverse Gamma distribution function. We test our method through numerical N-body simulations with a computational volume large enough (> 1 h^{-3} Gpc^3) to show that the linear power-spectra are nicely recovered over scales larger than >~20 h^{-1} Mpc, i.e. the relevant range where features imprinted by the baryon-acoustics oscillations (BAOs) appear.

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Estimating cosmic velocity fields from density fields and tidal tensors

In this work we investigate the nonlinear and nonlocal relation between cosmological density and peculiar velocity fields. Our goal is to provide an algorithm for the recon- struction of the nonlinear velocity field from the fully nonlinear density. We find that including the gravitational tidal field tensor using second order Lagrangian perturba- tion theory (2LPT) based upon an estimate of the linear component of the nonlinear density field significantly improves the estimate of the cosmic flow in comparison to linear theory not only in the low density, but also and more dramatically in the high density regions. In particular we test two estimates of the linear component: the log- normal model and the iterative Lagrangian linearisation. The present approach relies on a rigorous higher order Lagrangian perturbation theory analysis which incorpo- rates a nonlocal relation. It does not require additional fitting from simulations being in this sense parameter free, it is independent of statistical-geometrical optimisation and it is straightforward and efficient to compute. The method is demonstrated to yield an unbiased estimator of the velocity field on scales ~> 5 Mpc/h with closely Gaussian distributed errors. Moreover, the statistics of the divergence of the peculiar velocity field is extremely well recovered showing a good agreement with the true one from N-body simulations. The typical errors of about 10 km/s (1 sigma confidence intervals) are reduced by more than 80% with respect to linear theory in the scale range between 5 and 10 Mpc/h in high density regions (δ > 2). We also find that iterative Lagrangian linearisation is significantly superior in the low density regime with respect to the lognormal model.

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