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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.
Long-wavelength gravitational waves can induce significant temperature anisotropy in the cosmic microwave background. Distinguishing this from anisotropy induced by energy density fluctuations is critical for testing inflationary cosmology and theori
Delensing is an increasingly important technique to reverse the gravitational lensing of the cosmic microwave background (CMB) and thus reveal primordial signals the lensing may obscure. We present a first demonstration of delensing on Planck tempera
We report a search for signatures of cosmic strings in the the Cosmic Microwave Background data from the Wilkinson Microwave Anisotropy Probe. We used a digital filter designed to search for individual cosmic strings and found no evidence for them in
We compare the latest cosmic microwave background data with theoretical predictions including correlated adiabatic and CDM isocurvature perturbations with a simple power-law dependence. We find that there is a degeneracy between the amplitude of corr
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. Goo