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Benjamin D. Wandelt

Publications and source records attributed to Benjamin D. Wandelt.

At least 127 records · Page 7Linked to original sources

An optimal estimator for resonance bispectra in the CMB

We propose an (optimal) estimator for a CMB bispectrum containing logarithmically spaced oscillations. There is tremendous theoretical interest in such bispectra, and they are predicted by a plethora of models, including axion monodromy models of inflation and initial state modifications. The number of resolved logarithmical oscillations in the bispectrum is limited due to the discrete resolution of the multipole bispectrum. We derive a simple relation between the maximum number of resolved oscillations and the frequency. We investigate several ways to factorize the primordial bispectrum, and conclude that a one dimensional expansion in the sum of the momenta $\sum k_i = k_t$ is the most efficient and flexible approach. We compare the expansion to the exact result in multipole space and show for $ω_{\rm eff}=100$ that $\mathcal{O}(10^3)$ modes are sufficient for an accurate reconstruction. We compute the expected $σ_{f_{\rm NL}}$ and find that within an effective field theory (EFT) the overall signal to noise scales as $S/N\propto ω^{3/2}$. Using only the temperature data we find $S/N\sim\mathcal{O}(1-10^2)$ for the frequency domain set by the EFT.

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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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Cosmology: from theory to data, from data to theory

Cosmology has come a long way from being based on a small number of observations to being a data-driven precision science. We discuss the questions "What is observable?", "What in the Universe is knowable?" and "What are the fundamental limits to cosmological knowledge?". We then describe the methodology for investigation: theoretical hypotheses are used to model, predict and anticipate results; data is used to infer theory. We illustrate with concrete examples of principled analysis approaches from the study of cosmic microwave background anisotropies and surveys of large-scale structure, culminating in a summary of the highest precision probe to date of the physical origin of cosmic structures: the Planck 2013 constraints on primordial non-Gaussianity.

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On the observability of coupled dark energy with cosmic voids

Taking N-body simulations with volumes and particle densities tuned to match the SDSS DR7 spectroscopic main sample, we assess the ability of current void catalogs (e.g., Sutter et al. 2012b) to distinguish a model of coupled dark matter-dark energy from ΛCDM cosmology using properties of cosmic voids. Identifying voids with the VIDE toolkit, we find no statistically significant differences in the ellipticities, but find that coupling produces a population of significantly larger voids, possibly explaining the recent result of Tavasoli et al. (2013). In addition, we use the universal density profile of Hamaus et al. (2014) to quantify the relationship between coupling and density profile shape, finding that the coupling produces broader, shallower, undercompensated profiles for large voids by thinning the walls between adjacent medium-scale voids. We find that these differences are potentially measurable with existing void catalogs once effects from survey geometries and peculiar velocities are taken into account.

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Modeling cosmic void statistics

Understanding the internal structure and spatial distribution of cosmic voids is crucial when considering them as probes of cosmology. We present recent advances in modeling void density- and velocity-profiles in real space, as well as void two-point statistics in redshift space, by examining voids identified via the watershed transform in state-of-the-art $Λ$CDM n-body simulations and mock galaxy catalogs. The simple and universal characteristics that emerge from these statistics indicate the self-similarity of large-scale structure and suggest cosmic voids to be among the most pristine objects to consider for future studies on the nature of dark energy, dark matter and modified gravity.

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Past and present cosmic structure in the SDSS DR7 main sample

We present a chrono-cosmography project, aiming at the inference of the four dimensional formation history of the observed large scale structure from its origin to the present epoch. To do so, we perform a full-scale Bayesian analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS) Data Release 7 main galaxy sample, relying on a fully probabilistic, physical model of the non-linearly evolved density field. Besides inferring initial conditions from observations, our methodology naturally and accurately reconstructs non-linear features at the present epoch, such as walls and filaments, corresponding to high-order correlation functions generated by late-time structure formation. Our inference framework self-consistently accounts for typical observational systematic and statistical uncertainties such as noise, survey geometry and selection effects. We further account for luminosity dependent galaxy biases and automatic noise calibration within a fully Bayesian approach. As a result, this analysis provides highly-detailed and accurate reconstructions of the present density field on scales larger than $\sim~3$ Mpc$/h$, constrained by SDSS observations. This approach also leads to the first quantitative inference of plausible formation histories of the dynamic large scale structure underlying the observed galaxy distribution. The results described in this work constitute the first full Bayesian non-linear analysis of the cosmic large scale structure with the demonstrated capability of uncertainty quantification. Some of these results will be made publicly available along with this work. The level of detail of inferred results and the high degree of control on observational uncertainties pave the path towards high precision chrono-cosmography, the subject of simultaneously studying the dynamics and the morphology of the inhomogeneous Universe.

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Semi-blind Bayesian inference of CMB map and power spectrum

We present a new blind formulation of the Cosmic Microwave Background (CMB) inference problem. The approach relies on a phenomenological model of the multi-frequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that have previously been treated separately, such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, SMICA and ILC, and discuss possible future extensions.

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Searching for oscillations in the primordial power spectrum

A small deviation from scale invariance in the form of oscillations in the primordial correlation spectra has been predicted by various cosmological models. In this paper we review a recently developed method to search for these features in the data in a more effective way. By Taylor expanding the small features around the 'background' cosmology, we have shown we are able to improve the search for these features compared to previous analyses. In this short paper we will extend that work by combining this method with a multi nested sampler. We recover our previous findings and are able to do so in 192 CPU hours. We will also briefly discuss the possibility of a long wavelength feature in the data to alleviate some tension between CMB data and the LCDM+r concordance cosmology.

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Searching for Oscillations in the Primordial Power Spectrum: Constraints from Planck (Paper II)

We apply our recently developed code to search for resonance features in the Planck CMB temperature data. We search both for log spaced oscillations or linear spaced oscillations and compare our findings with results of our WMAP9 analysis and the Planck team analysis. While there are hints of log spaced resonant features present in the WMAP9 data, the significance of these features weaken with more data. With more accurate small scale measurements, we also find that the best fit frequency has shifted and the amplitude has been reduced. We confirm the presence of a several low frequency peaks, earlier identified by the Planck team, but with a better improvement of fit (delta chi^2 ~ 12). We further investigate this improvement by allowing the lensing potential to vary as well, showing mild correlation between the amplitude of the oscillations and the lensing amplitude. We find that the improvement of the fit increases even more (delta chi^2 ~ 14) for the low frequencies that modify the spectrum in a way that mimics the lensing effect. Since these features were not present in the WMAP data, they are primarily due to better measurements of Planck at small angular scales. For linear spaced oscillations we find a maximum delta chi^2 ~ 13 scanning two orders of magnitude in frequency space, and the biggest improvements are at extremely high frequencies. We recover a best fit frequency very close to the one found in WMAP9, which confirms that the fit improvement is driven by low l. Further comparisons with WMAP9 show Planck contains many more features, both for linear and log space oscillations, but with a smaller improvement of fit. We discuss the improvement as a function of the number of modes and study the effect of the 217 GHz map, which appears to drive most of the improvement for log spaced oscillations. We conclude that none of the detected features are statistically significant.

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A measurement of the Alcock-Paczynski effect using cosmic voids in the SDSS

We perform an Alcock-Paczynski test using stacked cosmic voids identified in the SDSS Data Release 7 main sample and Data Release 10 LOWZ and CMASS samples. We find ~1,500 voids out to redshift $0.6$ using a heavily modified and extended version of the watershed algorithm ZOBOV, which we call VIDE (Void IDentification and Examination). To assess the impact of peculiar velocities we use the mock void catalogs presented in Sutter et al. (2013). We find a constant uniform flattening of 14% along the line of sight when peculiar velocities are included. This flattening appears universal for all void sizes at all redshifts and for all tracer densities. We also use these mocks to identify an optimal stacking strategy. After correcting for systematic effects we find that our Alcock-Paczynski measurement leads to a preference of our best-fit value of $Ω_{\rm M}\sim 0.15$ over $Ω_{\rm M} = 1.0$ by a likelihood ratio of 10. Likewise, we find a factor of $4.5$ preference of the likelihood ratio for a $Λ$CDM $Ω_{\rm M} = 0.3$ model and a null measurement. Taken together, we find substantial evidence for the Alcock-Paczynski signal in our sample of cosmic voids. Our assessment using realistic mocks suggests that measurements with future SDSS releases and other surveys will provide tighter cosmological parameter constraints. The void-finding algorithm and catalogs used in this work will be made publicly available at http://www.cosmicvoids.net.

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Simulation of the analysis of interferometric microwave background polarization data

We present results from an end-to-end simulation pipeline interferometric observations of cosmic microwave background polarization. We use both maximum-likelihood and Gibbs sampling techniques to estimate the power spectrum. In addition, we use Gibbs sampling for image reconstruction from interferometric visibilities. The results indicate the level to which various systematic errors (e.g., pointing errors, gain errors, beam shape errors, cross- polarization) must be controlled in order to successfully detect and characterize primordial B modes as well as other scientific goals. In addition, we show that Gibbs sampling is an effective method of image reconstruction for interferometric data in other astrophysical contexts.

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Universal Density Profile for Cosmic Voids

We present a simple empirical function for the average density profile of cosmic voids, identified via the watershed technique in $Λ$CDM N-body simulations. This function is universal across void size and redshift, accurately describing a large radial range of scales around void centers with only two free parameters. In analogy to halo density profiles, these parameters describe the scale radius and the central density of voids. While we initially start with a more general four-parameter model, we find two of its parameters to be redundant, as they follow linear trends with the scale radius in two distinct regimes of the void sample, separated by its compensation scale. Assuming linear theory, we derive an analytic formula for the velocity profile of voids and find an excellent agreement with the numerical data as well. In our companion paper [Sutter et al., Mon. Not. R. Astron. Soc. 442, 462 (2014)] the presented density profile is shown to be universal even across tracer type, properly describing voids defined in halo and galaxy distributions of varying sparsity, allowing us to relate various void populations by simple rescalings. This provides a powerful framework to match theory and simulations with observational data, opening up promising perspectives to constrain competing models of cosmology and gravity.

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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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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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First measurement of gravitational lensing by cosmic voids in SDSS

We report the first measurement of the diminutive lensing signal arising from matter underdensities associated with cosmic voids. While undetectable individually, by stacking the weak gravitational shear estimates around 901 voids detected in SDSS DR7 by Sutter et al. (2012a), we find substantial evidence for a depression of the lensing signal compared to the cosmic mean. This depression is most pronounced at the void radius, in agreement with analytical models of void matter profiles. Even with the largest void sample and imaging survey available today, we cannot put useful constraints on the radial dark-matter void profile. We invite independent investigations of our findings by releasing data and analysis code to the public at https://github.com/pmelchior/void-lensing

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Sparse Inpainting and Isotropy

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

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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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The dark matter of galaxy voids

How do observed voids relate to the underlying dark matter distribution? To examine the spatial distribution of dark matter contained within voids identified in galaxy surveys, we apply Halo Occupation Distribution models representing sparsely and densely sampled galaxy surveys to a high-resolution N-body simulation. We compare these galaxy voids to voids found in the halo distribution, low-resolution dark matter, and high-resolution dark matter. We find that voids at all scales in densely sampled surveys - and medium- to large-scale voids in sparse surveys - trace the same underdensities as dark matter, but they are larger in radius by ~20%, they have somewhat shallower density profiles, and they have centers offset by ~0.4Rv rms. However, in void-to-void comparison we find that shape estimators are less robust to sampling, and the largest voids in sparsely sampled surveys suffer fragmentation at their edges. We find that voids in galaxy surveys always correspond to underdensities in the dark matter, though the centers may be offset. When this offset is taken into account, we recover almost identical radial density profiles between galaxies and dark matter. All mock catalogs used in this work are available at http://www.cosmicvoids.net.

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