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We present cosmological parameter constraints as estimated using the Bayesian BeyondPlanck (BP) analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data to final cosmological parameters. As a…

This paper presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlations of CMB data, using the hybrid approach employed previously: pixel-based at $\ell<30$ and a Gaussian approximation to the distribution of…

Cosmology and Nongalactic Astrophysics · Physics 2016-09-21 Planck Collaboration , N. Aghanim , M. Arnaud , M. Ashdown , J. Aumont , C. Baccigalupi , A. J. Banday , R. B. Barreiro , J. G. Bartlett , N. Bartolo , E. Battaner , K. Benabed , A. Benoît , A. Benoit-Lévy , J. -P. Bernard , M. Bersanelli , P. Bielewicz , J. J. Bock , A. Bonaldi , L. Bonavera , J. R. Bond , J. Borrill , F. R. Bouchet , F. Boulanger , M. Bucher , C. Burigana , R. C. Butler , E. Calabrese , J. -F. Cardoso , A. Catalano , A. Challinor , H. C. Chiang , P. R. Christensen , D. L. Clements , L. P. L. Colombo , C. Combet , A. Coulais , B. P. Crill , A. Curto , F. Cuttaia , L. Danese , R. D. Davies , R. J. Davis , P. de Bernardis , A. de Rosa , G. de Zotti , J. Delabrouille , F. -X. Désert , E. Di Valentino , C. Dickinson , J. M. Diego , K. Dolag , H. Dole , S. Donzelli , O. Doré , M. Douspis , A. Ducout , J. Dunkley , X. Dupac , G. Efstathiou , F. Elsner , T. A. Enßlin , H. K. Eriksen , J. Fergusson , F. Finelli , O. Forni , M. Frailis , A. A. Fraisse , E. Franceschi , A. Frejsel , S. Galeotta , S. Galli , K. Ganga , C. Gauthier , M. Gerbino , M. Giard , E. Gjerløw , J. González-Nuevo , K. M. Górski , S. Gratton , A. Gregorio , A. Gruppuso , J. E. Gudmundsson , J. Hamann , F. K. Hansen , D. L. Harrison , G. Helou , S. Henrot-Versillé , C. Hernández-Monteagudo , D. Herranz , S. R. Hildebrandt , E. Hivon , W. A. Holmes , A. Hornstrup , K. M. Huffenberger , G. Hurier , A. H. Jaffe , W. C. Jones , M. Juvela , E. Keihänen , R. Keskitalo , K. Kiiveri , J. Knoche , L. Knox , M. Kunz , H. Kurki-Suonio , G. Lagache , A. Lähteenmäki , J. -M. Lamarre , A. Lasenby , M. Lattanzi , C. R. Lawrence , M. Le Jeune , R. Leonardi , J. Lesgourgues , F. Levrier , A. Lewis , M. Liguori , P. B. Lilje , M. Lilley , M. Linden-Vørnle , V. Lindholm , M. López-Caniego , J. F. Macías-Pérez , B. Maffei , G. Maggio , D. Maino , N. Mandolesi , A. Mangilli , M. Maris , P. G. Martin , E. Martínez-González , S. Masi , S. Matarrese , P. R. Meinhold , A. Melchiorri , M. Migliaccio , M. Millea , S. Mitra , M. -A. Miville-Deschênes , A. Moneti , L. Montier , G. Morgante , D. Mortlock , S. Mottet , D. Munshi , J. A. Murphy , A. Narimani , P. Naselsky , F. Nati , P. Natoli , F. Noviello , D. Novikov , I. Novikov , C. A. Oxborrow , F. Paci , L. Pagano , F. Pajot , D. Paoletti , B. Partridge , F. Pasian , G. Patanchon , T. J. Pearson , O. Perdereau , L. Perotto , V. Pettorino , F. Piacentini , M. Piat , E. Pierpaoli , D. Pietrobon , S. Plaszczynski , E. Pointecouteau , G. Polenta , N. Ponthieu , G. W. Pratt , S. Prunet , J. -L. Puget , J. P. Rachen , M. Reinecke , M. Remazeilles , C. Renault , A. Renzi , I. Ristorcelli , G. Rocha , M. Rossetti , G. Roudier , B. Rouillé d'Orfeuil , J. A. Rubiño-Martín , B. Rusholme , L. Salvati , M. Sandri , D. Santos , M. Savelainen , G. Savini , D. Scott , P. Serra , L. D. Spencer , M. Spinelli , V. Stolyarov , R. Stompor , R. Sunyaev , D. Sutton , A. -S. Suur-Uski , J. -F. Sygnet , J. A. Tauber , L. Terenzi , L. Toffolatti , M. Tomasi , M. Tristram , T. Trombetti , M. Tucci , J. Tuovinen , G. Umana , L. Valenziano , J. Valiviita , F. Van Tent , P. Vielva , F. Villa , L. A. Wade , B. D. Wandelt , I. K. Wehus , D. Yvon , A. Zacchei , A. Zonca

Compressed Sensing suggests that the required number of samples for reconstructing a signal can be greatly reduced if it is sparse in a known discrete basis, yet many real-world signals are sparse in a continuous dictionary. One example is…

Information Theory · Computer Science 2015-07-24 Yuanxin Li , Yuejie Chi

In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties…

Computer Vision and Pattern Recognition · Computer Science 2023-05-04 David Reixach

We present significant improvements to our previous work on noise reduction in {\sl Herschel} observation maps by defining sparse filtering tools capable of handling, in a unified formalism, a significantly improved noise reduction as well…

We have compared the cosmic microwave background (CMB) temperature anisotropy maps made from one-year time ordered data (TOD) streams that simulated observations of the originally planned 100 GHz Planck Low Frequency Instrument (LFI). The…

In our previous study, we introduced a machine-learning technique, namely CMBFSCNN, for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual…

Cosmology and Nongalactic Astrophysics · Physics 2024-08-19 Ye-Peng Yan , Si-Yu Li , Guo-Jian Wang , Zirui Zhang , Jun-Qing Xia

We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task.…

Computer Vision and Pattern Recognition · Computer Science 2019-07-22 Saeed Anwar , Cong Phouc Huynh , Fatih Porikli

A problem of image denoising when images are corrupted by a non-stationary noise is considered in this paper. Since in practice no a priori information on noise is available, noise statistics should be pre-estimated for image denoising. In…

Image and Video Processing · Electrical Eng. & Systems 2021-09-27 Sheyda Ghanbaralizadeh Bahnemiri , Mykola Ponomarenko , Karen Egiazarian

We present a first internal delensing of CMB maps, both in temperature and polarization, using the public foreground-cleaned (SMICA) Planck 2015 maps. After forming quadratic estimates of the lensing potential, we use the corresponding…

Cosmology and Nongalactic Astrophysics · Physics 2017-05-24 Julien Carron , Antony Lewis , Anthony Challinor

One of the main problems for extracting the Cosmic Microwave Background (CMB) from submm/mm observations is to correct for the Galactic components, mainly synchrotron, free - free and thermal dust emission with the required accuracy.…

Cosmology and Nongalactic Astrophysics · Physics 2018-10-17 H. U. Nørgaard-Nielsen

Based on realistic simulations, we propose an hybrid method to reconstruct the lensing potential power spectrum, directly on PLANCK-like CMB frequency maps. It implies using a large galactic mask and dealing with a strong inhomogeneous…

Cosmology and Nongalactic Astrophysics · Physics 2015-03-20 S. Plaszczynski , A. Lavabre , L. Perotto , J. -L. Starck

This paper describes the processing applied to the HFI cleaned time-ordered data to produce photometrically calibrated maps. HFI observes the sky over a broad range of frequencies, from 100 to 857 GHz. To get the best accuracy on the…

Cosmology and Nongalactic Astrophysics · Physics 2014-10-29 Planck Collaboration , P. A. R. Ade , N. Aghanim , C. Armitage-Caplan , M. Arnaud , M. Ashdown , F. Atrio-Barandela , J. Aumont , C. Baccigalupi , A. J. Banday , R. B. Barreiro , E. Battaner , K. Benabed , A. Benoît , A. Benoit-Lévy , J. -P. Bernard , M. Bersanelli , B. Bertincourt , P. Bielewicz , J. Bobin , J. J. Bock , J. R. Bond , J. Borrill , F. R. Bouchet , F. Boulanger , M. Bridges , M. Bucher , C. Burigana , J. -F. Cardoso , A. Catalano , A. Challinor , A. Chamballu , R. -R. Chary , X. Chen , L. -Y Chiang , H. C. Chiang , P. R. Christensen , S. Church , D. L. Clements , S. Colombi , L. P. L. Colombo , C. Combet , F. Couchot , A. Coulais , B. P. Crill , A. Curto , F. Cuttaia , L. Danese , R. D. Davies , P. de Bernardis , A. de Rosa , G. de Zotti , J. Delabrouille , J. -M. Delouis , F. -X. Désert , C. Dickinson , J. M. Diego , H. Dole , S. Donzelli , O. Doré , M. Douspis , X. Dupac , G. Efstathiou , T. A. Enßlin , H. K. Eriksen , C. Filliard , F. Finelli , O. Forni , M. Frailis , E. Franceschi , S. Galeotta , K. Ganga , M. Giard , G. Giardino , Y. Giraud-Héraud , J. González-Nuevo , K. M. Górski , S. Gratton , A. Gregorio , A. Gruppuso , F. K. Hansen , D. Hanson , D. Harrison , G. Helou , S. Henrot-Versillé , C. Hernández-Monteagudo , D. Herranz , S. R. Hildebrandt , E. Hivon , M. Hobson , W. A. Holmes , A. Hornstrup , W. Hovest , K. M. Huffenberger , T. R. Jaffe , A. H. Jaffe , W. C. Jones , M. Juvela , E. Keihänen , R. Keskitalo , T. S. Kisner , R. Kneissl , J. Knoche , L. Knox , M. Kunz , H. Kurki-Suonio , G. Lagache , J. -M. Lamarre , A. Lasenby , R. J. Laureijs , C. R. Lawrence , M. Le Jeune , E. Lellouch , R. Leonardi , C. Leroy , J. Lesgourgues , M. Liguori , P. B. Lilje , M. Linden-Vørnle , M. López-Caniego , P. M. Lubin , J. F. Macías-Pérez , B. Maffei , N. Mandolesi , M. Maris , D. J. Marshall , P. G. Martin , E. Martínez-González , S. Masi , S. Matarrese , F. Matthai , L. Maurin , P. Mazzotta , P. McGehee , P. R. Meinhold , A. Melchiorri , L. Mendes , A. Mennella , M. Migliaccio , S. Mitra , M. -A. Miville-Deschênes , A. Moneti , L. Montier , R. Moreno , G. Morgante , D. Mortlock , D. Munshi , J. A. Murphy , P. Naselsky , F. Nati , P. Natoli , C. B. Netterfield , H. U. Nørgaard-Nielsen , F. Noviello , D. Novikov , I. Novikov , S. Osborne , C. A. Oxborrow , F. Paci , L. Pagano , F. Pajot , R. Paladini , D. Paoletti , B. Partridge , F. Pasian , G. Patanchon , O. Perdereau , L. Perotto , F. Perrotta , F. Piacentini , M. Piat , E. Pierpaoli , D. Pietrobon , S. Plaszczynski , E. Pointecouteau , G. Polenta , N. Ponthieu , L. Popa , T. Poutanen , G. W. Pratt , G. Prézeau , S. Prunet , J. -L. Puget , J. P. Rachen , M. Reinecke , M. Remazeilles , C. Renault , S. Ricciardi , T. Riller , I. Ristorcelli , G. Rocha , C. Rosset , G. Roudier , B. Rusholme , D. Santos , G. Savini , E. P. S. Shellard , L. D. Spencer , J. -L. Starck , V. Stolyarov , R. Stompor , R. Sudiwala , R. Sunyaev , F. Sureau , D. Sutton , A. -S. Suur-Uski , J. -F. Sygnet , J. A. Tauber , D. Tavagnacco , S. Techene , L. Terenzi , M. Tomasi , M. Tristram , M. Tucci , G. Umana , L. Valenziano , J. Valiviita , B. Van Tent , P. Vielva , F. Villa , N. Vittorio , L. A. Wade , B. D. Wandelt , D. Yvon , A. Zacchei , A. Zonca

We study a blind deconvolution problem on graphs, which arises in the context of localizing a few sources that diffuse over networks. While the observations are bilinear functions of the unknown graph filter coefficients and sparse input…

Signal Processing · Electrical Eng. & Systems 2024-09-19 Chang Ye , Gonzalo Mateos

The High Frequency Instrument of Planck will map the entire sky in the millimeter and sub-millimeter domain from 100 to 857 GHz with unprecedented sensitivity to polarization ($\Delta P/T_{\tiny cmb} \sim 4\cdot 10^{-6}$) at 100, 143, 217…

In this paper, we propose a Bayesian MAP estimator for solving the deconvolution problems when the observations are corrupted by Poisson noise. Towards this goal, a proper data fidelity term (log-likelihood) is introduced to reflect the…

Applications · Statistics 2011-03-14 François-Xavier Dupé , Jalal Fadili , Jean-Luc Starck

The Low Frequency Instrument (LFI) is an array of pseudo-correlation radiometers on board the Planck satellite, the ESA mission dedicated to precision measurements of the Cosmic Microwave Background. The LFI covers three bands centred at…

Image denoising is a fundamental problem in image processing whose primary objective is to remove the noise while preserving the original image structure. In this work, we proposed a new architecture for image denoising. We have used…

Image and Video Processing · Electrical Eng. & Systems 2019-03-25 Sutanu Bera , Avisek Lahiri , Prabir Kumar Biswas

Marine Controlled Source Electromagnetic (CSEM) is employed both in large-scale geophysical applications as well as within exploration of hydrocarbons and gas hydrates. Due to the diffusive character of the EM field only very low…

Geophysics · Physics 2022-11-08 Feng-Ping Li , Vemund Stenbekk Thorkildsen , Leiv-J Gelius , Jian-Hua Yue

LuSEE-Night is a pathfinder radio telescope on the lunar far side employing four 3-m monopole antennas arranged as two horizontal cross pseudo-dipoles on a rotational stage and sensitive to the radio sky in the 1-50 MHz frequency band.…

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