Wavelet domain Bayesian denoising of string signal in the cosmic microwave background
arXiv:0811.1267 · doi:10.1111/j.1365-2966.2009.14978.x
Abstract
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.
16 pages, 11 figures. Version 2 matches version accepted for publication in MNRAS. Changes include substantial clarifications on our approach and a significant reduction of manuscript length
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- Astronomical Image Denoising Using Dictionary Learning
- Searching for Cosmic Strings in CMB Anisotropy Maps using Wavelets and Curvelets
- Equivalence of solutions between the four-dimensional novel and regularized EGB theories in a cylindrically symmetric spacetime
- Compressed sensing reconstruction of a string signal from interferometric observations of the cosmic microwave background
- All sky CMB map from cosmic strings integrated Sachs-Wolfe effect
- On the computation of directional scale-discretized wavelet transforms on the sphere
- Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background
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- On the inverse problem of finding cosmic strings and other topological defects
- Level Crossing Analysis of Cosmic Microwave Background Radiation: A method for detecting cosmic strings
- Cosmic String Detection with Tree-Based Machine Learning
- Peak-peak correlations in the cosmic background radiation from cosmic strings
- Cosmic Strings-induced CMB anisotropies in light of Weighted Morphology