Wavelet domain Bayesian denoising of string signal in the cosmic microwave background

D. K. Hammond; Yves Wiaux; Pierre Vandergheynst

doi:10.1111/j.1365-2966.2009.14978.x

Wavelet domain Bayesian denoising of string signal in the cosmic microwave background

D. K. Hammond, Yves Wiaux, Pierre Vandergheynst

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

Abstract

An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background. A Bayesian approach is taken, based on modelling 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 cosmic microwave background anisotropies, as well as instrumental noise.

Original language	English
Pages (from-to)	1317-1332
Number of pages	16
Journal	Monthly Notices of the Royal Astronomical Society
Volume	398
Issue number	3
DOIs	https://doi.org/10.1111/j.1365-2966.2009.14978.x
Publication status	Published - 21 Sept 2009

Keywords

Cosmic microwave background
Methods: Data analysis
Techniques: image processing

ASJC Scopus subject areas

Space and Planetary Science
Astronomy and Astrophysics

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10.1111/j.1365-2966.2009.14978.x

Cite this

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abstract = "An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background. A Bayesian approach is taken, based on modelling 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 cosmic microwave background anisotropies, as well as instrumental noise.",

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AB - An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background. A Bayesian approach is taken, based on modelling 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 cosmic microwave background anisotropies, as well as instrumental noise.

KW - Cosmic microwave background

KW - Methods: Data analysis

KW - Techniques: image processing

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Wavelet domain Bayesian denoising of string signal in the cosmic microwave background

Abstract

Keywords

ASJC Scopus subject areas

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