Wavelets and WMAP non-Gaussianity

We study the statistical properties of the 1st year WMAP data on different scales using the spherical mexican hat wavelet transform. Consistent with the results of Vielva et al. (2003) we find a deviation from Gaussianity in the form of kurtosis of wavelet coefficients on $3-4^\circ$ scales in the southern Galactic hemisphere. This paper extends the work of Vielva et al. as follows. We find that the non-Gaussian signal shows up more strongly in the form of a larger than expected number of cold pixels and also in the form of scale-scale correlations amongst wavelet coefficients. We establish the robustness of the non-Gaussian signal under more wide-ranging assumptions regarding the Galactic mask applied to the data and the noise statistics. This signal is unlikely to be due to the usual quadratic term parametrized by the non-linearity parameter $f_{NL}$. We use the skewness of the spherical mexican hat wavelet coefficients to constrain $f_{NL}$ with the 1st year WMAP data. Our results constrain $f_{NL}$ to be $50\pm 80$ at 68% confidence, and less than 280 at 99% confidence.Comment: 22 pages, 10 figures, ApJ accepte

General CMB bispectrum analysis using wavelets and separable modes

In this paper we combine partial-wave (`modal') methods with a wavelet analysis of the CMB bispectrum. Our implementation exploits the advantages of both approaches to produce robust, reliable and efficient estimators which can constrain the amplitude of arbitrary primordial bispectra. This will be particularly important for upcoming surveys such as \emph{Planck}. A key advantage is the computational efficiency of calculating the inverse covariance matrix in wavelet space, producing an error bar which is close to optimal. We verify the efficacy and robustness of the method by applying it to WMAP7 data, finding \fnllocal=38.4 \pm 23.6 and \fnlequil=-119.2 \pm 123.6

On what scale should inflationary observables be constrained?

We examine the choice of scale at which constraints on inflationary observables are presented. We describe an implementation of the hierarchy of inflationary consistency equations which ensures that they remain enforced on different scales, and then seek to optimize the scale for presentation of constraints on marginalized inflationary parameters from WMAP3 data. For models with spectral index running, we find a strong variation of the constraints through the range of observational scales available, and optimize by finding the scale which decorrelates constraints on the spectral index n_S and the running. This scale is k=0.017 Mpc^{-1}, and gives a reduction by a factor of more than four in the allowed parameter area in the n_S-r plane (r being the tensor-to-scalar ratio) relative to k=0.002 Mpc^{-1}. These optimized constraints are similar to those obtained in the no-running case. We also extend the analysis to a larger compilation of data, finding essentially the same conclusions.Comment: 7 pages RevTeX4 with 9 figures included. v2: References added, new section added analyzing additional datasets alongside WMAP3. v3: Minor corrections to match version accepted by PR

Model selection in cosmology

Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters. A common model selection question is to ask when new data require introduction of an additional parameter, describing a newly discovered physical effect. We review model selection statistics, then focus on the Bayesian evidence, which implements Bayesian analysis at the level of models rather than parameters. We describe our CosmoNest code, the first computationally efficient implementation of Bayesian model selection in a cosmological context. We apply it to recent WMAP satellite data, examining the need for a perturbation spectral index differing from the scaleinvariant (Harrison–Zel'dovich) case

Looking for Cosmological Alfven Waves in WMAP Data

A primordial cosmological magnetic field induces and supports vorticity or Alfven waves, which in turn generate cosmic microwave background (CMB) anisotropies. A homogeneous primordial magnetic field with fixed direction induces correlations between the $a_{l-1,m}$ and $a_{l+1,m}$ multipole coefficients of the CMB temperature anisotropy field. We discuss the constraints that can be placed on the strength of such a primordial magnetic field using CMB anisotropy data from the WMAP experiment. We place 3 $\sigma$ upper limits on the strength of the magnetic field of $B < 15$ nG for vector perturbation spectral index $n=-5$ and $B<1.7$ nG for $n=-7$.Comment: 14 pages, 3 figures, minor changes, references added, ApJ, in pres

Observational Bounds on Modified Gravity Models

Modified gravity provides a possible explanation for the currently observed cosmic accelaration. In this paper, we study general classes of modified gravity models. The Einstein-Hilbert action is modified by using general functions of the Ricci and the Gauss-Bonnet scalars, both in the metric and in the Palatini formalisms. We do not use an explicit form for the functions, but a general form with a valid Taylor expansion up to second order about redshift zero in the Riemann-scalars. The coefficients of this expansion are then reconstructed via the cosmic expansion history measured using current cosmological observations. These are the quantities of interest for theoretical considerations relating to ghosts and instabilities. We find that current data provide interesting constraints on the coefficients. The next-generation dark energy surveys should shrink the allowed parameter space for modifed gravity models quite dramatically.Comment: 23 pages, 5 figures, uses RevTe

A Comparative Study of Dark Energy Constraints from Current Observational Data

We examine how dark energy constraints from current observational data depend on the analysis methods used: the analysis of Type Ia supernovae (SNe Ia), and that of galaxy clustering data. We generalize the flux-averaging analysis method of SNe Ia to allow correlated errors of SNe Ia, in order to reduce the systematic bias due to weak lensing of SNe Ia. We find that flux-averaging leads to larger errors on dark energy and cosmological parameters if only SN Ia data are used. When SN Ia data (the latest compilation by the SNLS team) are combined with WMAP 7 year results (in terms of our Gaussian fits to the probability distributions of the CMB shift parameters), the latest Hubble constant (H_0) measurement using the Hubble Space Telescope (HST), and gamma ray burst (GRB) data, flux-averaging of SNe Ia increases the concordance with other data, and leads to significantly tighter constraints on the dark energy density at z=1, and the cosmic curvature \Omega_k. The galaxy clustering measurements of H(z=0.35)r_s(z_d) and r_s(z_d)/D_A(z=0.35) (where H(z) is the Hubble parameter, D_A(z) is the angular diameter distance, and r_s(z_d) is the sound horizon at the drag epoch) by Chuang & Wang (2011) are consistent with SN Ia data, given the same pirors (CMB+H_0+GRB), and lead to significantly improved dark energy constraints when combined. Current data are fully consistent with a cosmological constant and a flat universe.Comment: 11 pages, 9 figures. Slightly revised version, to appear in PRD. Supernova flux-averaging code available at http://www.nhn.ou.edu/~wang/SNcode

Model-Independent Constraints on Dark Energy Density from Flux-averaging Analysis of Type Ia Supernova Data

We reconstruct the dark energy density $\rho_X(z)$ as a free function from current type Ia supernova (SN Ia) data (Tonry et al. 2003; Barris et al. 2003; Knop et al. 2003), together with the Cosmic Microwave Background (CMB) shift parameter from CMB data (WMAP, CBI, and ACBAR), and the large scale structure (LSS) growth factor from 2dF galaxy survey data. We parametrize $\rho_X(z)$ as a continuous function, given by interpolating its amplitudes at equally spaced $z$ values in the redshift range covered by SN Ia data, and a constant at larger $z$ (where $\rho_X(z)$ is only weakly constrained by CMB data). We assume a flat universe, and use the Markov Chain Monte Carlo (MCMC) technique in our analysis. We find that the dark energy density $\rho_X(z)$ is constant for 0 \la z \la 0.5 and increases with redshift $z$ for 0.5 \la z \la 1 at 68.3% confidence level, but is consistent with a constant at 95% confidence level. For comparison, we also give constraints on a constant equation of state for the dark energy. Flux-averaging of SN Ia data is required to yield cosmological parameter constraints that are free of the bias induced by weak gravitational lensing \citep{Wang00b}. We set up a consistent framework for flux-averaging analysis of SN Ia data, based on \cite{Wang00b}. We find that flux-averaging of SN Ia data leads to slightly lower $\Omega_m$ and smaller time-variation in $\rho_X(z)$. This suggests that a significant increase in the number of SNe Ia from deep SN surveys on a dedicated telescope \citep{Wang00a} is needed to place a robust constraint on the time-dependence of the dark energy density.Comment: Slightly revised in presentation, ApJ accepted. One color figure shows rho_X(z) reconstructed from dat

Detecting and distinguishing topological defects in future data from the CMBPol satellite

The proposed CMBPol mission will be able to detect the imprint of topological defects on the CMB provided the contribution is sufficiently strong. We quantify the detection threshold for cosmic strings and for textures, and analyze the satellite's ability to distinguish between these different types of defects. We also assess the level of danger of misidentification of a defect signature as from the wrong defect type or as an effect of primordial gravitational waves. A 0.002 fractional contribution of cosmic strings to the CMB temperature spectrum at multipole ten, and similarly a 0.001 fractional contribution of textures, can be detected and correctly identified at the 3 level. We also confirm that a tensor contribution of r=0.0018 can be detected at over 3, in agreement with the CMBPol mission concept study. These results are supported by a model selection analysis