Sparse point-source removal for full-sky CMB experiments: application to WMAP 9-year data

Sureau, F.; Starck, Jean-Luc; Bobin, J.; Paykari, P.; Rassat, A.

doi:10.1051/0004-6361/201322706

Cited by 8 publications

(6 citation statements)

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“…was found to be extremely efficient for different inverse problems in astrophysics, such as an estimation of the cosmic microwave background (CMB; Bobin et al 2014), estimation of compact sources in CMB missions (Sureau et al 2014), recovery of a weak-lensing map (Lanusse et al 2016), or reconstruction of a radio-interferomety image (Garsden et al 2015). We compare our deconvolution techniques with this sparse-deconvolution approach here.…”

Section: Deconvolution Before Deep Learningmentioning

confidence: 99%

Deep learning for a space-variant deconvolution in galaxy surveys

Sureau

Lechat

Starck

2020

A&A

Self Cite

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The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

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Section: Deconvolution Before Deep Learningmentioning

confidence: 99%

Deep learning for a space-variant deconvolution in galaxy surveys

Sureau

Lechat

Starck

2020

A&A

Self Cite

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“…Sparse signal modeling in the wavelet domain is very well adapted to efficiently describe the statistics of non-Gaussian and nonstationary processes. This has been shown to significant improve the extraction of foreground components such as galactic foregrounds and point sources (Bobin et al 2013;Sureau et al 2014).…”

Section: Sparse Component Separation For Cmb Reconstructionmentioning

confidence: 99%

Cosmic microwave background reconstruction from WMAP andPlanckPR2 data

Bobin

Sureau

Starck

2016

A&A

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We describe a new estimate of the cosmic microwave background (CMB) intensity map reconstructed by a joint analysis of the full Planck 2015 data (PR2) and nine years of WMAP data. The proposed map provides more than a mere update of the CMB map introduced in a previous paper since it benefits from an improvement of the component separation method L-GMCA (Local-Generalized Morphological Component Analysis), which facilitates efficient separation of correlated components. Based on the most recent CMB data, we further confirm previous results showing that the proposed CMB map estimate exhibits appealing characteristics for astrophysical and cosmological applications: i) it is a full-sky map as it did not require any inpainting or interpolation postprocessing; ii) foreground contamination is very low even on the galactic center; and iii) the map does not exhibit any detectable trace of thermal Sunyaev-Zel'dovich contamination. We show that its power spectrum is in good agreement with the Planck PR2 official theoretical best-fit power spectrum. Finally, following the principle of reproducible research, we provide the codes to reproduce the L-GMCA, which makes it the only reproducible CMB map.

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“…The 1 norm in the regularisation term is known to reinforce the sparsity of the solution, see Starck et al (2015b) for a review on sparsity. Sparsity was found extremely efficient for different inverse problems in astrophysics such as Cosmic Microwave Background (CMB) estimation (Bobin et al 2014), compact sources estimation in CMB missions (Sureau et al 2014), weak lensing map recovery (Lanusse, F. et al 2016) or radio-interferomety image reconstruction (Garsden et al 2015). We will compare in this work our deconvolution techniques with such sparse deconvolution approach.…”

Section: Deconvolution Before Deep Learningmentioning

confidence: 99%

Deep Learning for space-variant deconvolution in galaxy surveys

Sureau,

Lechat,

Starck

2019

Preprint

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Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We investigate in this paper how Deep Learning (DL) could be used to perform this task. We employ a U-NET Deep Neural Network (DNN) architecture to learn in a supervised setting parameters adapted for galaxy image processing and study two strategies for deconvolution. The first approach is a post-processing of a mere Tikhonov deconvolution with closed form solution and the second one is an iterative deconvolution framework based on the Alternating Direction Method of Multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and PSFs show that our two approaches outperforms standard techniques based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on Tikhonov deconvolution leads to the most accurate results except for ellipticity errors at high signal to noise ratio where the ADMM approach performs slightly better, is also more computation-time efficient to process a large number of galaxies, and is therefore recommended in this scenario.

show abstract

Sparse point-source removal for full-sky CMB experiments: application to WMAP 9-year data

Cited by 8 publications

References 50 publications

Deep learning for a space-variant deconvolution in galaxy surveys

Deep learning for a space-variant deconvolution in galaxy surveys

Cosmic microwave background reconstruction from WMAP andPlanckPR2 data

Deep Learning for space-variant deconvolution in galaxy surveys

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