arXiv subjects

Guilhem Lavaux

Publications and source records attributed to Guilhem Lavaux.

At least 91 records · Page 5Linked to original sources

arXiv2017

Reconstructing the gravitational field of the local universe

Tests of gravity at the galaxy scale are in their infancy. As a first step to systematically uncovering the gravitational significance of galaxies, we map three fundamental gravitational variables -- the Newtonian potential, acceleration and curvature -- over the galaxy environments of the local universe to a distance of approximately 200 Mpc. Our method combines the contributions from galaxies in an all-sky redshift survey, halos from an N-body simulation hosting low-luminosity objects, and linear and quasi-linear modes of the density field. We use the ranges of these variables to determine the extent to which galaxies expand the scope of generic tests of gravity and are capable of constraining specific classes of model for which they have special significance. Finally, we investigate the improvements afforded by upcoming galaxy surveys.

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arXiv2017

Bayesian power-spectrum inference with foreground and target contamination treatment

This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power-spectra and three dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block sampling procedures for unknown coefficients of foreground and target contamination templates to the previously presented ARES framework for Bayesian large scale structure analyses. As a result the method infers jointly and fully self-consistently three dimensional density fields, cosmological power-spectra, luminosity dependent galaxy biases, noise levels of respective galaxy distributions and coefficients for a set of a priori specified foreground templates. In addition this fully Bayesian approach permits detailed quantification of correlated uncertainties amongst all inferred quantities and correctly marginalizes over observational systematic effects. We demonstrate the validity and efficiency of our approach in obtaining unbiased estimates of power-spectra via applications to realistic mock galaxy observations subject to stellar contamination and dust extinction. While simultaneously accounting for galaxy biases and unknown noise levels our method reliably and robustly infers three dimensional density fields and corresponding cosmological power-spectra from deep galaxy surveys. Further our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power-spectrum. An effect amounting up to $10$ percent correlations and anti-correlations across large ranges in Fourier space.

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arXiv2017

The phase-space structure of nearby dark matter as constrained by the SDSS

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings and foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.

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arXiv2017

Wiener filter reloaded: fast signal reconstruction without preconditioning

We present a high performance solution to the Wiener filtering problem via a formulation that is dual to the recently developed messenger technique. This new dual messenger algorithm, like its predecessor, efficiently calculates the Wiener filter solution of large and complex data sets without preconditioning and can account for inhomogeneous noise distributions and arbitrary mask geometries. We demonstrate the capabilities of this scheme in signal reconstruction by applying it on a simulated cosmic microwave background (CMB) temperature data set. The performance of this new method is compared to that of the standard messenger algorithm and the preconditioned conjugate gradient (PCG) approach, using a series of well-known convergence diagnostics and their processing times, for the particular problem under consideration. This variant of the messenger algorithm matches the performance of the PCG method in terms of the effectiveness of reconstruction of the input angular power spectrum and converges smoothly to the final solution. The dual messenger algorithm outperforms the standard messenger and PCG methods in terms of execution time, as it runs to completion around 2 and 3-4 times faster than the respective methods, for the specific problem considered.

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arXiv2016

Constraints on Cosmology and Gravity from the Dynamics of Voids

The Universe is mostly composed of large and relatively empty domains known as cosmic voids, whereas its matter content is predominantly distributed along their boundaries. The remaining material inside them, either dark or luminous matter, is attracted to these boundaries and causes voids to expand faster and to grow emptier over time. Using the distribution of galaxies centered on voids identified in the Sloan Digital Sky Survey and adopting minimal assumptions on the statistical motion of these galaxies, we constrain the average matter content $Ω_\mathrm{m}=0.281\pm0.031$ in the Universe today, as well as the linear growth rate of structure $f/b=0.417\pm0.089$ at median redshift $\bar{z}=0.57$, where $b$ is the galaxy bias ($68\%$ C.L.). These values originate from a percent-level measurement of the anisotropic distortion in the void-galaxy cross-correlation function, $\varepsilon = 1.003\pm0.012$, and are robust to consistency tests with bootstraps of the data and simulated mock catalogs within an additional systematic uncertainty of half that size. They surpass (and are complementary to) existing constraints by unlocking cosmological information on smaller scales through an accurate model of nonlinear clustering and dynamics in void environments. As such, our analysis furnishes a powerful probe of deviations from Einstein's general relativity in the low-density regime which has largely remained untested so far. We find no evidence for such deviations in the data at hand.

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arXiv2016

Comparing cosmic web classifiers using information theory

We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-web, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

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arXiv2015

Unmasking the Masked Universe: the 2M++ catalogue through Bayesian eyes

This work describes a full Bayesian analysis of the Nearby Universe as traced by galaxies of the 2M++ survey. The analysis is run in two sequential steps. The first step self-consistently derives the luminosity dependent galaxy biases, the power-spectrum of matter fluctuations and matter density fields within a Gaussian statistic approximation. The second step makes a detailed analysis of the three dimensional Large Scale Structures, assuming a fixed bias model and a fixed cosmology. This second step allows for the reconstruction of both the final density field and the initial conditions at z=1000 assuming a fixed bias model. From these, we derive fields that self-consistently extrapolate the observed large scale structures. We give two examples of these extrapolation and their utility for the detection of structures: the visibility of the Sloan Great Wall, and the detection and characterization of the Local Void using DIVA, a Lagrangian based technique to classify structures.

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arXiv2015

Probabilistic cartography of the large-scale structure

The BORG algorithm is an inference engine that derives the initial conditions given a cosmological model and galaxy survey data, and produces physical reconstructions of the underlying large-scale structure by assimilating the data into the model. We present the application of BORG to real galaxy catalogs and describe the primordial and late-time large-scale structure in the considered volumes. We then show how these results can be used for building various probabilistic maps of the large-scale structure, with rigorous propagation of uncertainties. In particular, we study dynamic cosmic web elements and secondary effects in the cosmic microwave background.

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arXiv2015

Probing cosmology and gravity with redshift-space distortions around voids

Cosmic voids in the large-scale structure of the Universe affect the peculiar motions of objects in their vicinity. Although these motions are difficult to observe directly, the clustering pattern of their surrounding tracers in redshift space is influenced in a unique way. This allows to investigate the interplay between densities and velocities around voids, which is solely dictated by the laws of gravity. With the help of $N$-body simulations and derived mock-galaxy catalogs we calculate the average density fluctuations around voids identified with a watershed algorithm in redshift space and compare the results with the expectation from general relativity and the $Λ$CDM model. We find linear theory to work remarkably well in describing the dynamics of voids. Adopting a Bayesian inference framework, we explore the full posterior of our model parameters and forecast the achievable accuracy on measurements of the growth rate of structure and the geometric distortion through the Alcock-Paczynski effect. Systematic errors in the latter are reduced from $\sim15\%$ to $\sim5\%$ when peculiar velocities are taken into account. The relative parameter uncertainties in galaxy surveys with number densities comparable to the SDSS MAIN (CMASS) sample probing a volume of $1h^{-3}{\rm Gpc}^3$ yield $σ_{f/b}\left/(f/b)\right.\sim2\%$ ($20\%$) and $σ_{D_AH}/D_AH\sim0.2\%$ ($2\%$), respectively. At this level of precision the linear-theory model becomes systematics dominated, with parameter biases that fall beyond these values. Nevertheless, the presented method is highly model independent; its viability lies in the underlying assumption of statistical isotropy of the Universe.

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arXiv2015

Real-space density profile reconstruction of stacked voids

We present a non-parametric, model-independent method to reconstruct the spherical density profiles of void stacks in real space, without redshift-space distortions. Our method uses the expected spherical symmetry of stacked voids to build the shape of the spherical density profile of cosmic voids in real space without any assumption about the cosmological model. We test the reconstruction algorithm with both a toy model and a full dark matter simulation. We present the result for the simulation: the reconstruction of the spherical density profile for a simulated stacked void in real space. We also present a first application of the algorithm to reconstruct real cosmic void stacks density profiles in real space from the Sloan Digital Sky Survey (Sutter et al. 2012b). We discuss capabilities of the algorithm and possible future improvements. Reconstructed density profiles from real voids open the way to the study of the spherically averaged dynamical structure of voids.

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arXiv2015

Bayesian inference of CMB gravitational lensing

The Planck satellite, along with several ground based telescopes, have mapped the cosmic microwave background (CMB) at sufficient resolution and signal-to-noise so as to allow a detection of the subtle distortions due to the gravitational influence of the intervening matter distribution. A natural modeling approach is to write a Bayesian hierarchical model for the lensed CMB in terms of the unlensed CMB and the lensing potential. So far there has been no feasible algorithm for inferring the posterior distribution of the lensing potential from the lensed CMB map. We propose a solution that allows efficient Markov Chain Monte Carlo sampling from the joint posterior of the lensing potential and the unlensed CMB map using the Hamiltonian Monte Carlo technique. The main conceptual step in the solution is a re-parameterization of CMB lensing in terms of the lensed CMB and the "inverse lensing" potential. We demonstrate a fast implementation on simulated data including noise and a sky cut, that uses a further acceleration based on a very mild approximation of the inverse lensing potential. We find that the resulting Markov Chain has short correlation lengths and excellent convergence properties, making it promising for application to high resolution CMB data sets of the future.

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arXiv2015

Cosmological parameters from the comparison of peculiar velocities with predictions from the 2M++ density field

Peculiar velocity measurements are the only tool available in the low-redshift Universe for mapping the large-scale distribution of matter and can thus be used to constrain cosmology. Using redshifts from the 2M++ redshift compilation, we reconstruct the density of galaxies within 200 Mpc/h, allowing for the first time good sampling of important superclusters such as the Shapley Concentration. We compare the predicted peculiar velocities from 2M++ to Tully-Fisher and SNe peculiar velocities. We find a value of $β^* \equiv Ω_{\rm{m}}^{0.55}/b^* = 0.431 \pm 0.021$, suggesting $Ω_{\rm{m}}^{0.55}σ_{\rm{8,lin}} = 0.401 \pm 0.024$, in good agreement with other probes. The predicted peculiar velocity of the Local Group arising from the 2M++ volume alone is $540 \pm 40$ km/s, towards $l = 268 \pm 4, b= 38 \pm 6$, only $10^\circ$ out of alignment with the Cosmic Microwave Background dipole. To account for velocity contributions arising from sources outside the 2M++ volume, we fit simultaneously for $β^*$ and an external bulk flow in our analysis. We find that an external bulk flow is preferred at the 5.1$σ$ level, and the best fit has a velocity of $159\pm23$ km/s towards $l=304 \pm 11, b = 6 \pm 13$. Finally, the predicted bulk flow of a 50 Mpc/h Gaussian-weighted volume centred on the Local Group is $230 \pm 30$ km/s, in the direction $l=293\pm 8, b = 14 \pm 10$, in agreement with predictions from $Λ$CDM.

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arXiv2014

Testing cosmic geometry without dynamic distortions using voids

We propose a novel technique to probe the expansion history of the Universe based on the clustering statistics of cosmic voids. In particular, we compute their two-point statistics in redshift space on the basis of realistic mock galaxy catalogs and apply the Alcock-Paczynski test. In contrast to galaxies, we find void auto-correlations to be marginally affected by peculiar motions, providing a model-independent measure of cosmological parameters without systematics from redshift-space distortions. Because only galaxy-galaxy and void-galaxy correlations have been considered in these types of studies before, the presented method improves both statistical and systematic uncertainties on the product of angular diameter distance and Hubble rate, furnishing the potentially cleanest probe of cosmic geometry available to date.

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arXiv2014

VIDE: The Void IDentification and Examination toolkit

We present VIDE, the Void IDentification and Examination toolkit, an open-source Python/C++ code for finding cosmic voids in galaxy redshift surveys and N-body simulations, characterizing their properties, and providing a platform for more detailed analysis. At its core, VIDE uses a substantially enhanced version of ZOBOV (Neyinck 2008) to calculate a Voronoi tessellation for estimating the density field and a performing a watershed transform to construct voids. Additionally, VIDE provides significant functionality for both pre- and post-processing: for example, vide can work with volume- or magnitude-limited galaxy samples with arbitrary survey geometries, or dark matter particles or halo catalogs in a variety of common formats. It can also randomly subsample inputs and includes a Halo Occupation Distribution model for constructing mock galaxy populations. VIDE uses the watershed levels to place voids in a hierarchical tree, outputs a summary of void properties in plain ASCII, and provides a Python API to perform many analysis tasks, such as loading and manipulating void catalogs and particle members, filtering, plotting, computing clustering statistics, stacking, comparing catalogs, and fitting density profiles. While centered around ZOBOV, the toolkit is designed to be as modular as possible and accommodate other void finders. VIDE has been in development for several years and has already been used to produce a wealth of results, which we summarize in this work to highlight the capabilities of the toolkit. VIDE is publicly available at http://bitbucket.org/cosmicvoids/vide public and http://www.cosmicvoids.net.

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arXiv2014

Voids in the SDSS DR9: observations, simulations, and the impact of the survey mask

We present and study cosmic voids identified using the watershed void finder VIDE in the Sloan Digital Sky Survey Data Release 9, compare these voids to ones identified in mock catalogs, and assess the impact of the survey mask on void statistics such as number functions, ellipticity distributions, and radial density profiles. The nearly 1,000 identified voids span three nearly volume-limited samples from redshift z = 0.43 to 0.7. For comparison we use 98 of the publicly available 2LPT-based mock galaxy catalogs of Manera et al., and also generate our own mock catalogs by applying a Halo Occupation Distribution model to an N-body simulation. We find that the mask reduces the number density of voids at all scales by a factor of three and slightly skews the relative size distributions. This engenders an increase in the mean ellipticity by roughly 30%. However, we find that radial density profiles are largely robust to the effects of the mask. We see excellent agreement between the data and both mock catalogs, and find no tension between the observed void properties and the properties derived from ΛCDM simulations. We have added the void catalogs from both data and mock galaxy populations discussed in this work to the Public Cosmic Void Catalog at http://www.cosmicvoids.net.

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arXiv2014

Sparse sampling, galaxy bias, and voids

To study the impact of sparsity and galaxy bias on void statistics, we use a single large-volume, high-resolution N-body simulation to compare voids in multiple levels of subsampled dark matter, halo populations, and mock galaxies from a Halo Occupation Distribution model tuned to different galaxy survey densities. We focus our comparison on three key observational statistics: number functions, ellipticity distributions, and radial density profiles. We use the hierarchical tree structure of voids to interpret the impacts of sampling density and galaxy bias, and theoretical and empirical functions to describe the statistics in all our sample populations. We are able to make simple adjustments to theoretical expectations to offer prescriptions for translating from analytics to the void properties measured in realistic observations. We find that sampling density has a much larger effect on void sizes than galaxy bias. At lower tracer density, small voids disappear and the remaining voids are larger, more spherical, and have slightly steeper profiles. When a proper lower mass threshold is chosen, voids in halo distributions largely mimic those found in galaxy populations, except for ellipticities, where galaxy bias leads to higher values. We use the void density profile of Hamaus et al. (2014) to show that voids follow a self-similar and universal trend, allowing simple translations between voids studied in dark matter and voids identified in galaxy surveys. We have added the mock void catalogs used in this work to the Public Cosmic Void Catalog at http://www.cosmicvoids.net.

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arXiv2014

Matrix-free Large Scale Bayesian inference in cosmology

In this work we propose a new matrix-free implementation of the Wiener sampler which is traditionally applied to high dimensional analysis when signal covariances are unknown. Specifically, the proposed method addresses the problem of jointly inferring a high dimensional signal and its corresponding covariance matrix from a set of observations. Our method implements a Gibbs sampling adaptation of the previously presented messenger approach, permitting to cast the complex multivariate inference problem into a sequence of uni-variate random processes. In this fashion, the traditional requirement of inverting high dimensional matrices is completely eliminated from the inference process, resulting in an efficient algorithm that is trivial to implement. Using cosmic large scale structure data as a showcase, we demonstrate the capabilities of our Gibbs sampling approach by performing a joint analysis of three dimensional density fields and corresponding power-spectra from Gaussian mock catalogues. These tests clearly demonstrate the ability of the algorithm to accurately provide measurements of the three dimensional density field and its power-spectrum and corresponding uncertainty quantification. Moreover, these tests reveal excellent numerical and statistical efficiency which will generally render the proposed algorithm a valuable addition to the toolbox of large scale Bayesian inference in cosmology and astrophysics.

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arXiv2014

Cosmology with Void-Galaxy Correlations

Galaxy bias, the unknown relationship between the clustering of galaxies and the underlying dark matter density field is a major hurdle for cosmological inference from large-scale structure. While traditional analyses focus on the absolute clustering amplitude of high-density regions mapped out by galaxy surveys, we propose a relative measurement that compares those to the underdense regions, cosmic voids. On the basis of realistic mock catalogs we demonstrate that cross correlating galaxies and voids opens up the possibility to calibrate galaxy bias and to define a static ruler thanks to the observable geometric nature of voids. We illustrate how the clustering of voids is related to mass compensation and show that volume-exclusion significantly reduces the degree of stochasticity in their spatial distribution. Extracting the spherically averaged distribution of galaxies inside voids from their cross correlations reveals a remarkable concordance with the mass-density profile of voids.

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