279,336 research outputs found

    Efficient Cosmological Parameter Estimation from Microwave Background Anisotropies

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    We revisit the issue of cosmological parameter estimation in light of current and upcoming high-precision measurements of the cosmic microwave background power spectrum. Physical quantities which determine the power spectrum are reviewed, and their connection to familiar cosmological parameters is explicated. We present a set of physical parameters, analytic functions of the usual cosmological parameters, upon which the microwave background power spectrum depends linearly (or with some other simple dependence) over a wide range of parameter values. With such a set of parameters, microwave background power spectra can be estimated with high accuracy and negligible computational effort, vastly increasing the efficiency of cosmological parameter error determination. The techniques presented here allow calculation of microwave background power spectra 10510^5 times faster than comparably accurate direct codes (after precomputing a handful of power spectra). We discuss various issues of parameter estimation, including parameter degeneracies, numerical precision, mapping between physical and cosmological parameters, and systematic errors, and illustrate these considerations with an idealized model of the MAP experiment.Comment: 22 pages, 12 figure

    Estimation of Primordial Spectrum with post-WMAP 3 year data

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    In this paper we implement an improved (error sensitive) Richardson-Lucy deconvolution algorithm on the measured angular power spectrum from the WMAP 3 year data to determine the primordial power spectrum assuming different points in the cosmological parameter space for a flat LCDM cosmological model. We also present the preliminary results of the cosmological parameter estimation by assuming a free form of the primordial spectrum, for a reasonably large volume of the parameter space. The recovered spectrum for a considerably large number of the points in the cosmological parameter space has a likelihood far better than a `best fit' power law spectrum up to \Delta \chi^2_{eff} \approx -30. We use Discrete Wavelet Transform (DWT) for smoothing the raw recovered spectrum from the binned data. The results obtained here reconfirm and sharpen the conclusion drawn from our previous analysis of the WMAP 1st year data. A sharp cut off around the horizon scale and a bump after the horizon scale seem to be a common feature for all of these reconstructed primordial spectra. We have shown that although the WMAP 3 year data prefers a lower value of matter density for a power law form of the primordial spectrum, for a free form of the spectrum, we can get a very good likelihood to the data for higher values of matter density. We have also shown that even a flat CDM model, allowing a free form of the primordial spectrum, can give a very high likelihood fit to the data. Theoretical interpretation of the results is open to the cosmology community. However, this work provides strong evidence that the data retains discriminatory power in the cosmological parameter space even when there is full freedom in choosing the primordial spectrum.Comment: 13 pages, 4 figures, uses Revtex4, new analysis and results, references added, matches version accepted to Phys. Rev.

    Two-dimensional spectrum estimation using the radon transform

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    An alternative approach to two-dimensional power spectrum estimation incorporating the Radon transform in conjunction with each of the one-dimensional periodogram, Blackman-Tukey, and Autoregressive parameter estimation algorithms is examined. The Radon transform is used to express a two-dimensional data set in terms of its projections onto a set of one-dimensional radial lines, effectively reducing the two-dimensional estimation problem to a series of one-dimensional problems. The resulting two-dimensional power spectrum estimates are compared to the known power spectra for a variety of data types. The Radon transform approach combined with autoregressive parameter estimation can provide a high-resolution power spectrum estimate, effectively surpassing the resolution limitations of the Fourier methods without the cumbersome implementations of the more direct high resolution estimation methods in two dimensions

    A Bayesian study of the primordial power spectrum from a novel closed universe model

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    We constrain the shape of the primordial power spectrum using recent measurements of the cosmic microwave background (CMB) from the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data and other high-resolution CMB experiments. We also include observations of the matter power spectrum from the luminous red galaxy (LRG) subset DR7 of the Sloan Digital Sky Survey (SDSS). We consider two different models of the primordial power spectrum. The first is the standard nearly scale-invariant spectrum in the form of a generalised power-law parameterised in terms of the spectral amplitude AsA_{\rm s}, the spectral index nsn_{\rm s} and (possibly) the running parameter nrunn_{\rm run}. The second spectrum is derived from the Lasenby and Doran (LD) model. The LD model is based on the restriction of the total conformal time available in a closed Universe and the predicted primordial power spectrum depends upon just two parameters. An important feature of the LD spectrum is that it naturally incorporates an exponential fall-off on large scales, which might provide a possible explanation for the lower-than-expected power observed at low multipoles in the CMB. In addition to parameter estimation, we compare both models using Bayesian model selection. We find there is a significant preference for the LD model over a simple power-law spectrum for a CMB-only dataset, and over models with an equal number of parameters for all the datasets considered.Comment: minor corrections to match accepted version to MNRA

    Precise Estimation of Cosmological Parameters Using a More Accurate Likelihood Function

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    The estimation of cosmological parameters from a given data set requires a construction of a likelihood function which, in general, has a complicated functional form. We adopt a Gaussian copula and constructed a copula likelihood function for the convergence power spectrum from a weak lensing survey. We show that the parameter estimation based on the Gaussian likelihood erroneously introduces a systematic shift in the confidence region, in particular for a parameter of the dark energy equation of state w. Thus, the copula likelihood should be used in future cosmological observations.Comment: 5 pages, 3 figures. Maches version published by the Physical Review Letter

    Fast Parameter Estimation from the CMB Power Spectrum

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    The statistical properties of a map of the primary fluctuations in the cosmic microwave background (CMB) may be specified to high accuracy by a few thousand power spectra measurements, provided the fluctuations are gaussian, yet the number of parameters relevant for the CMB is probably no more than about 10-20. There is consequently a large degree of redundancy in the power spectrum data. In this paper, we show that the MOPED data compression technique can reduce the CMB power spectrum measurements to about 10-20 numbers (one for each parameter), from which the cosmological parameters can be estimated virtually as accurately as from the complete power spectrum. This offers opportunities for very fast parameter estimation from real and simulated CMB skies, with accurate likelihood calculations at Planck resolution being speeded up by a factor of around five hundred million.Comment: version to appear in MNRA
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