Large-scale Structure Data Sets Research Articles

Consistent clustering and lensing of SDSS-III BOSS galaxies with an extended abundance matching formalism

ABSTRACT Several analyses have shown that Λ cold dark matter-based models cannot jointly describe the clustering (GC) and galaxy–galaxy lensing (GGL) of galaxies in the Sloan Digital Sky Survey-III (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS), which is commonly known as the ‘lensing-is-low problem’. In this work, we show that an extension of Subhalo Abundance Matching, dubbed SHAMe, successfully solves this problem. First, we show that this model accurately reproduces the GC and GGL of a mock galaxy sample in the TNG300 hydrodynamic simulation with properties analogous to those of BOSS galaxies. Then, we switch our attention to observed BOSS galaxies at z = 0.31−0.43, and we attempt to reproduce their GC and GGL by evaluating SHAMe on two different simulations: one adopting best-fitting cosmological parameters from Planck and the other from weak gravitational lensing surveys (Low S8), where the amplitude of matter fluctuations is lower for the latter. We find excellent agreement between SHAMe predictions and observations for both cosmologies, indicating that the lensing-is-low problem originates from approximations in previous theoretical descriptions of the data. The main difference between SHAMe results in these cosmologies is the level of galaxy assembly bias, which is approximately 20 per cent and 10 per cent for Planck and Low S8, respectively. These results highlight the dangers of employing oversimplified models to analyse current large-scale structure data sets, and the need for realistic yet flexible descriptions of the galaxy–halo connection.

Searching for bounce signature in the early universe from current and future large-scale structure surveys

ABSTRACT The bounce scenario has been an interesting research topic in cosmology, due to its ability to resolve both the singularity problem and the trans-Planckian issue, which are left from the standard inflationary theory. In previous works, we considered an inflationary cosmology with a preceding non-singular bounce and found that this model could suppress the primordial power spectrum at large scales and leave the signature on the angular power spectra of cosmic microwave background (CMB). In this work, we extend this analysis to the large-scale structure (LSS) measurements. Firstly, we consider the angular power spectrum of current LSS data sets, such as the 2MPZ, SDSS-DR12, and DES Y3 galaxy surveys at low redshifts and the NVSS radio survey at high redshifts, and do not obtain good constraint on the model parameters, due to the precision limitation at large scales. When we include the Planck CMB measurements, the constraints become a little bit better: the amplitude Ar = 0.8 ± 0.2 and the slope ${\rm log_{10}}(k_B)=-2.6^{+0.3}_{-1.0}$ at 68 per cent confidence level. In order to evaluate the constraining ability of future LSS surveys, we forecast the clustering measurements, such as the galaxy angular power spectrum and the galaxy lensing shear power spectrum, based on the China Space Station Telescope photometric survey. We find that the standard deviations of model parameters will be significantly shrunk, ΔAr = 0.1 and Δlog10(kB) = 0.1, due to the high precision measurements. Finally, we consider the bounce feature and the primordial non-Gaussianity from inflation theory simultaneously and find that in the bounce inflationary model the limit on fNL will be weaker than that obtained in the standard inflationary model, due to the strong degeneracy among parameters.

Hybrid Pℓ(k): general, unified, non-linear matter power spectrum in redshift space

Constraints on gravity and cosmology will greatly benefit from performing joint clustering and weak lensing analyses on large-scale structure data sets. Utilising non-linear information coming from small physical scales can greatly enhance these constraints. At the heart of these analyses is the matter power spectrum. Here we employ a simple method, dubbed “Hybrid Pℓ(k)”, based on the Gaussian Streaming Model (GSM), to calculate the quasi non-linear redshift space matter power spectrum multipoles. This employs a fully non-linear and theoretically general prescription for the matter power spectrum. We test this approach against comoving Lagrangian acceleration simulation measurements performed in GR, DGP and f(R) gravity and find that our method performs comparably or better to the dark matter TNS redshift space power spectrum model {for dark matter. When comparing the redshift space multipoles for halos, we find that the Gaussian approximation of the GSM with a linear bias and a free stochastic term, N, is competitive to the TNS model.} Our approach offers many avenues for improvement in accuracy as well as further unification under the halo model.

Improved renormalization group computation of likelihood functions for cosmological data sets

Evaluation of likelihood functions for cosmological large scale structure data sets (including CMB, galaxy redshift surveys, etc.) naturally involves marginalization, i.e., integration, over an unknown underlying random signal field. Recently, I showed how a renormalization group method can be used to carry out this integration efficiently by first integrating out the smallest scale structure, i.e., localized structure on the scale of differences between nearby data cells, then combining adjacent cells in a coarse graining step, then repeating this process over and over until all scales have been integrated. Here I extend the formulation in several ways in order to reduce the prefactor on the method's linear scaling with data set size. The key improvement is showing how to integrate out the difference between specific adjacent cells before summing them in the coarse graining step, compared to the original formulation in which small-scale fluctuations were integrated more generally. I suggest some other improvements in details of the scheme, including showing how to perform the integration around a maximum likelihood estimate for the underlying random field. In the end, an accurate likelihood computation for a million-cell Gaussian test data set runs in two minutes on my laptop, with room for further optimization and straightforward parallelization.

Bias loop corrections to the galaxy bispectrum

Combination of the power spectrum and bispectrum is a powerful way of breaking degeneracies between galaxy bias and cosmological parameters, enabling us to maximize the constraining power from galaxy surveys. Recent cosmological constraints treat the power spectrum and bispectrum on an uneven footing: they include one-loop bias corrections for the power spectrum but not the bispectrum. To bridge this gap, we develop the galaxy bias description up to fourth order in perturbation theory, conveniently expressed through a basis of Galilean invariants that clearly split contributions that are local and nonlocal in the second derivatives of the linear gravitational potential. In addition, we consider relevant contributions from short-range nonlocality (higher-derivative terms), stress-tensor corrections and stochasticity. To sidestep the usual renormalization of bias parameters that complicates predictions beyond leading order, we recast the bias expansion in terms of multipoint propagators, which take a simple form in our split-basis with loop corrections depending only on bias parameters corresponding to nonlocal operators. We show how to take advantage of Galilean invariance to compute the time evolution of bias and present results for the fourth-order parameters for the first time. We also discuss the possibilities of reducing the bias parameter space by using the evolution of bias and exploiting degeneracies between various bias contributions in the large-scale limit. Our baseline model allows to verify these simplifications for any application to large-scale structure data sets.

Renormalization group computation of likelihood functions for cosmological data sets

I show how a renormalization group (RG) method can be used to incrementally integrate the information in cosmological large-scale structure data sets (including CMB, galaxy redshift surveys, etc.). I show numerical tests for Gaussian fields, where the method allows arbitrarily close to exact computation of the likelihood function in order $\sim N$ time, even for problems with no symmetry, compared to $N^3$ for brute force linear algebra (where $N$ is the number of data points -- to be fair, methods already exist to solve the Gaussian problem in at worst $N \log N$ time, and this method will not necessarily be faster in practice). The method requires no sampling or other Monte Carlo (random) element. Non-linearity/non-Gaussianity can be accounted for to the extent that terms generated by integrating out small scale modes can be projected onto a sufficient basis, e.g., at least in the sufficiently perturbative regime. The formulas to evaluate are straightforward and require no understanding of quantum field theory, but this paper may also serve as a pedagogical introduction to Wilsonian RG for astronomers.

The promising future of a robust cosmological neutrino mass measurement

We forecast the sensitivity of thirty-five different combinations of future Cosmic Microwave Background and Large Scale Structure data sets to cosmological parameters and to the total neutrino mass. We work under conservative assumptions accounting for uncertainties in the modelling of systematics. In particular, for galaxy redshift surveys, we remove the information coming from non-linear scales. We use Bayesian parameter extraction from mock likelihoods to avoid Fisher matrix uncertainties. Our grid of results allows for a direct comparison between the sensitivity of different data sets. We find that future surveys will measure the neutrino mass with high significance and will not be substantially affected by potential parameter degeneracies between neutrino masses, the density of relativistic relics, and a possible time-varying equation of state of Dark Energy.

Cosmological discordances. II. Hubble constant, Planck and large-scale-structure data sets

We examine systematically the (in)consistency between cosmological constraints as obtained from various current data sets of the expansion history, Large Scale Structure (LSS), and Cosmic Microwave Background (CMB) from Planck. We run (dis)concordance tests within each set and across the sets using a recently introduced index of inconsistency (IOI) capable of dissecting inconsistencies between two or more data sets. First, we compare the constraints on $H_0$ from five different methods and find that the IOI drops from 2.85 to 0.88 (on Jeffreys' scales) when the local $H_0$ measurements is removed. This seems to indicate that the local measurement is an outlier, thus favoring a systematics-based explanation. We find a moderate inconsistency (IOI=2.61) between Planck temperature and polarization. We find that current LSS data sets including WiggleZ, SDSS RSD, CFHTLenS, CMB lensing and SZ cluster count, are consistent one with another and when all combined. However, we find a persistent moderate inconsistency between Planck and individual or combined LSS probes. For Planck TT+lowTEB versus individual LSS probes, the IOI spans the range 2.92--3.72 and increases to 3.44--4.20 when the polarization data is added in. The joint LSS versus the combined Planck temperature and polarization has an IOI of 2.83 in the most conservative case. But if Planck lowTEB is added to the joint LSS to constrain $\tau$ and break degeneracies, the inconsistency between Planck and joint LSS data increases to the high-end of the moderate range with IOI=4.81. Whether due to systematic effects in the data or to the underlying model, these inconsistencies need to be resolved. Finally, we perform forecast calculations using LSST and find that the discordance between Planck and future LSS data, if it persists as present, can rise up to a high IOI of 17, thus falling in the strong range of inconsistency. (Abridged).

Reconstructing cosmic growth with kinetic Sunyaev-Zel’dovich observations in the era of stage IV experiments

Future ground-based CMB experiments will generate competitive large-scale structure datasets by precisely characterizing CMB secondary anisotropies over a large fraction of the sky. We describe a method for constraining the growth rate of structure to sub-1% precision out to $z\approx 1$, using a combination of galaxy cluster peculiar velocities measured using the kinetic Sunyaev-Zel'dovich (kSZ) effect, and the velocity field reconstructed from galaxy redshift surveys. We consider only thermal SZ-selected cluster samples, which will consist of $\mathcal{O}(10^4-10^5)$ sources for Stage 3 and 4 CMB experiments respectively. Three different methods for separating the kSZ effect from the primary CMB are compared, including a novel blind "constrained realization" method that improves signal-to-noise by a factor of $\sim 2$ over a commonly-used aperture photometry technique. Measurements of the integrated tSZ $y$-parameter are used to break the kSZ velocity-optical depth degeneracy, and the effects of including CMB polarization and SZ profile uncertainties are also considered. A combination of future Stage 4 experiments should be able to measure the product of the growth and expansion rates, $\alpha\equiv f H$, to better than 1% in bins of $\Delta z = 0.1$ out to $z \approx 1$ -- competitive with contemporary redshift-space distortion constraints from galaxy surveys.

Accounting for baryonic effects in cosmic shear tomography: determining a minimal set of nuisance parameters using PCA

Systematic uncertainties that have been subdominant in past large-scale structure (LSS) surveys are likely to exceed statistical uncertainties of current and future LSS data sets, potentially limiting the extraction of cosmological information. Here we present a general framework (PCA marginalization) to consistently incorporate systematic effects into a likelihood analysis. This technique naturally accounts for degeneracies between nuisance parameters and can substantially reduce the dimension of the parameter space that needs to be sampled. As a practical application, we apply PCA marginalization to account for baryonic physics as an uncertainty in cosmic shear tomography. Specifically, we use CosmoLike to run simulated likelihood analyses on three independent sets of numerical simulations, each covering a wide range of baryonic scenarios differing in cooling, star formation, and feedback mechanisms. We simulate a Stage III (Dark Energy Survey) and Stage IV (Large Synoptic Survey Telescope/Euclid) survey and find a substantial bias in cosmological constraints if baryonic physics is not accounted for. We then show that PCA marginalization (employing at most 3 to 4 nuisance parameters) removes this bias. Our study demonstrates that it is possible to obtain robust, precise constraints on the dark energy equation of state even in the presence of large levels of systematic uncertainty in astrophysical processes. We conclude that the PCA marginalization technique is a powerful, general tool for addressing many of the challenges facing the precision cosmology program.

CROSS-CORRELATIONS AS A COSMOLOGICAL CARBON MONOXIDE DETECTOR

We present a new procedure to measure the large-scale carbon monoxide (CO) emissions across cosmic history. As a tracer of large-scale structure (LSS), the CO gas content as a function of redshift can be quantified by its three-dimensional fluctuation power spectra. Furthermore, cross-correlating CO emission with other LSS tracers offers a way to measure the emission as a function of scale and redshift. Here we introduce the model relevant for such a cross-correlation measurement between CO and other LSS tracers, and between different CO rotational lines. We propose a novel use of cosmic microwave background (CMB) data and attempt to extract redshifted CO emissions embedded in the Wilkinson Microwave Anisotropy Probe (WMAP) data set. We cross-correlate the all-sky WMAP7 data with LSS data sets, namely, the photometric quasar sample and the luminous red galaxy sample from the Sloan Digital Sky Survey Data Releases 6 and 7, respectively. We are unable to detect a cross-correlation signal with either CO(1–0) or CO(2–1) lines, mainly due to the instrumental noise in the WMAP data. However, we are able to rule out models more than three times greater than our more optimistic model. We discuss the cross-correlation signal from the thermal Sunyaev–Zeldovich effect and dust as potential contaminants, and quantify their impact for our CO measurements. We discuss forecasts for current CMB experiments and a hypothetical future CO-focused experiment, and propose to cross-correlate CO temperature data with the Hobby–Eberly Telescope Dark Energy Experiment Lyα-emitter sample, for which a signal-to-noise ratio of 58 is possible.

The shape of the primordial power spectrum: A last stand before Planck data

We present a minimally parametric reconstruction of the primordial power spectrum using the most recent cosmic microwave background and large-scale structure data sets. Our goal is to constrain the shape of the power spectrum while simultaneously avoiding strong theoretical priors and over-fitting of the data. We find no evidence for any departure from a power-law spectral index. We also find that an exact scale-invariant power spectrum is disfavored by the data, but this conclusion is weaker than the corresponding result assuming a theoretically-motivated power-law spectral index prior. The reconstruction shows that better data are crucial to justify the adoption of such a strong theoretical prior observationally. These results can be used to determine the robustness of our present knowledge when compared with forthcoming precision data from Planck.

Correlation of CMB with large-scale structure. I. Integrated Sachs-Wolfe tomography and cosmological implications

We cross correlate large-scale structure (LSS) observations from a number of surveys with cosmic microwave background (CMB) anisotropies from the Wilkinson Microwave Anisotropy Probe (WMAP) to investigate the integrated Sachs-Wolfe (ISW) effect as a function of redshift, covering $z\ensuremath{\sim}0.1--2.5$. Our main goal is to go beyond reporting detections towards developing a reliable likelihood analysis that allows one to determine cosmological constraints from ISW observations. With this in mind we spend a considerable amount of effort in determining the redshift-dependent bias and redshift distribution ($b(z)\ifmmode\times\else\texttimes\fi{}dN/dz$) of these samples by matching with spectroscopic observations where available, and analyzing autopower spectra and cross-power spectra between the samples. Because of wide redshift distributions of some of the data sets we do not assume a constant-bias model, in contrast to previous work on this subject. We only use the LSS data sets for which we can extract such information reliably and as a result the data sets we use are 2-Micron All Sky Survey (2MASS) samples, Sloan Digital Sky Survey (SDSS) photometric Luminous Red Galaxies, SDSS photometric quasars, and NRAO VLA Sky Survey (NVSS) radio sources. We make a joint analysis of all samples constructing a full covariance matrix, which we subsequently use for cosmological parameter fitting. We report a $3.7\ensuremath{\sigma}$ detection of ISW combining all the data sets. We do not find significant evidence for an ISW signal at $z>1$, in agreement with theoretical expectation in the $\ensuremath{\Lambda}\mathrm{CDM}$ model. We combine the ISW likelihood function with weak lensing of CMB (hereafter Paper II [C. M. Hirata, S. Ho, N. Padmanabhan, U. Seljak, and N. A. Bahcall, arXiv:0801.0644.]) and CMB power spectrum to constrain the equation of state of dark energy and the curvature of the Universe. While ISW does not significantly improve the constraints in the simplest six-parameter flat $\ensuremath{\Lambda}\mathrm{CDM}$ model, it improves constraints on seven-parameter models with curvature by a factor of 3.2 (relative to WMAP alone) to ${\ensuremath{\Omega}}_{K}=\ensuremath{-}{0.004}_{\ensuremath{-}0.020}^{+0.014}$, and with dark energy equation of state by 15% to $w=\ensuremath{-}{1.01}_{\ensuremath{-}0.40}^{+0.30}$ [posterior median with ``$1\ensuremath{\sigma}$'' (16th--84th percentile) range]. A software package for calculating the ISW likelihood function can be downloaded at http://www.astro.princeton.edu/~shirley/ISW_WL.html.

Constraints on the inflationary expansion from three-year WMAP, small scale CMBanisotropies and large scale structure data sets

We present the constraints on inflationary parameters in a flatΛCDM universe obtained from the Wilkinson Microwave Anisotropy Probe (WMAP)three-year data release, plus smaller scale cosmic microwave background (CMB)and two large scale structure (LSS) data sets, 2dF and the Sloan Digital SkySurvey (SDSS) (treated separately). We use a Markov chain Monte Carlo (MCMC)technique combined with an analytic description of the inflationary spectra in termsof the horizon flow functions (HFF). By imposing a consistency condition forthe tensor-to-scalar ratio, we study the constraints on both single field standardinflation and on inflation with the violation of the null energy condition, whichleads to a blue spectrum for gravitational waves. For standard inflation, theconstraint on the tensor-to-scalar ratio we obtain from CMB data and 2dF05 isr0.01<0.26 at2σ confidence level. Without the consistency condition between the tensor-to-scalar ratio and thetensor slope, the constraint on the tensor amplitude is not significantly changed, but theconstraints on the HFFs are significantly relaxed. We then show that when the third HFFϵ3 is allowed to be non-zero and to be of order unity, a large negative (at2σ) value for the running of the scalar spectral index in standard inflation is found in any setof data we consider.