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Register a new userWavelet domain Bayesian denoising of string signal in the cosmic microwave background
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An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background (CMB). A Bayesian approach is taken, based on modeling the string signal in the wavelet domain with generalized Gaussian distributions. Good performance of the algorithm is demonstrated by simulated experiments at arcminute resolution under noise conditions including primary and secondary CMB anisotropies, as well as instrumental noise.
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Cosmic strings are a well-motivated extension to the standard cosmological model and could induce a subdominant component in the anisotropies of the cosmic microwave background (CMB), in addition to the standard inflationary component. The detection of strings, while observationally challenging, would provide a direct probe of physics at very high energy scales. We develop a new framework for cosmic string inference, constructing a Bayesian analysis in wavelet space where the string-induced CMB component has distinct statistical properties to the standard inflationary component. Our wavelet-Bayesian framework provides a principled approach to compute the posterior distribution of the string tension $Gmu$ and the Bayesian evidence ratio comparing the string model to the standard inflationary model. Furthermore, we present a technique to recover an estimate of any string-induced CMB map embedded in observational data. Using Planck-like simulations we demonstrate the application of our framework and evaluate its performance. The method is sensitive to $Gmu sim 5 times 10^{-7}$ for Nambu-Goto string simulations that include an integrated Sachs-Wolfe (ISW) contribution only and do not include any recombination effects, before any parameters of the analysis are optimised. The sensitivity of the method compares favourably with other techniques applied to the same simulations.
We propose an algorithm for the reconstruction of the signal induced by cosmic strings in the cosmic microwave background (CMB), from radio-interferometric data at arcminute resolution. Radio interferometry provides incomplete and noisy Fourier measurements of the string signal, which exhibits sparse or compressible magnitude of the gradient due to the Kaiser-Stebbins (KS) effect. In this context the versatile framework of compressed sensing naturally applies for solving the corresponding inverse problem. Our algorithm notably takes advantage of a model of the prior statistical distribution of the signal fitted on the basis of realistic simulations. Enhanced performance relative to the standard CLEAN algorithm is demonstrated by simulated observations under noise conditions including primary and secondary CMB anisotropies.
We introduce a new technique to detect the discrete temperature steps that cosmic strings might have left in the cosmic microwave background (CMB) anisotropy map. The technique provides a validity test on the pattern search of cosmic strings that could serve as the groundwork for future pattern searches. The detecting power of the technique is only constrained by two unavoidable features of CMB data: (1) the finite pixelization of the sky map and (2) the Gaussian fluctuation from instrumental noise and primordial anisotropy. We set the upper limit on the cosmic string parameter as $Gmulesssim 3.7times 10^{-6}$ at the 95% confidence level (CL) and find that the amplitude of the temperature step has to be greater than $44mu K$ in order to be detected for the {it{Wilkinson Microwave Anisotropy Probe (WMAP)}} 3 year data.
A recently proposed mechanism for large-scale structure in string cosmology --based on massless axionic seeds-- is further analyzed and extended to the acoustic-peak region. Existence, structure, and normalization of the peaks turn out to depend crucially on the overall evolution of extra dimensions during the pre-big bang phase: conversely, precise cosmic microwave background anisotropy data in the acoustic-peak region will provide, within the next decade, a window on string-theorys extra dimensions before their eventual compactification.
To correctly analyse data sets from current microwave detection technology, one is forced to estimate the sky signal and experimental noise simultaneously. Given a time-ordered data set we propose a formalism and method for estimating the signal and associated errors without prior knowledge of the noise power spectrum. We derive the method using a Bayesian formalism and relate it to the standard methods; in particular we show how this leads to a change in the estimate of the noise covariance matrix of the sky signal. We study the convergence and accuracy of the method on two mock observational strategies and discuss its application to a currently-favoured calibration procedure.
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