PySAP: Python Sparse Data Analysis Package for Multidisciplinary Image Processing


Authors: S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, Chaithya G. R., S. Starck, B. Sarthou, H. Cherkaoui, P.Ciuciu, J-L. Starck
Journal: Astronomy and Computing
Year: 2020
DOI: 10.1016/j.ascom.2020.100402
Download: ADS | arXiv


We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.


PySAP Code

Euclid: Reconstruction of weak-lensing mass maps for non-Gaussianity studies

Euclid: Reconstruction of weak-lensing mass maps for non-Gaussianity studies

Authors: S. Pires, V. Vandenbussche, V. Kansal, R. Bender, L. Blot, D. Bonino, A. Boucaud, J. Brinchmann, V. Capobianco, J. Carretero, M. Castellano, S. Cavuoti, R. Clédassou, G. Congedo, L. Conversi, L. Corcione, F. Dubath, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, F. Grupp, F. Hormuth, S. Kermiche, M. Knabenhans, R. Kohley, B. Kubik, M. Kunz, S. Ligori, P.B. Lilje, I. Lloro, E. Maiorano, O. Marggraf, R. Massey, G. Meylan, C. Padilla, S. Paltani, F. Pasian, M. Poncet, D. Potter, F. Raison, J. Rhodes, M. Roncarelli, R. Saglia, P. Schneider, A. Secroun, S. Serrano, J. Stadel, P. Tallada Crespí, I. Tereno, R. Toledo-Moreo, Y. Wang
Journal: Astronomy and Astrophysics
Year: 2020

ADS | arXiv 



Weak lensing, namely the deflection of light by matter along the line of sight, has proven to be an efficient method to constrain models of structure formation and reveal the nature of dark energy. So far, most weak lensing studies have focused on the shear field that can be measured directly from the ellipticity of background galaxies. However, within the context of forthcoming full-sky weak lensing surveys such as Euclid, convergence maps (mass maps) offer an important advantage over shear fields in terms of cosmological exploitation. While carrying the same information, the lensing signal is more compressed in the convergence maps than in the shear field, simplifying otherwise computationally expensive analyses, for instance non-Gaussianity studies. However, the inversion of the non-local shear field requires accurate control of systematic effects due to holes in the data field, field borders, noise and the fact that the shear is not a direct observable (reduced shear). In this paper, we present the two mass inversion methods that are being included in the official Euclid data processing pipeline: the standard Kaiser & Squires method (KS) and a new mass inversion method (KS+) that aims to reduce the information loss during the mass inversion. This new method is based on the KS methodology and includes corrections for mass mapping systematic effects. The results of the KS+ method are compared to the original implementation of the KS method in its simplest form, using the Euclid Flagship mock galaxy catalogue. In particular, we estimate the quality of the reconstruction by comparing the two-point correlation functions, third- and fourth-order moments obtained from shear and convergence maps, and we analyse each systematic effect independently and simultaneously. We show that the KS+ method reduces substantially the errors on the two-point correlation function and moments compared to the KS method. In particular, we show that the errors introduced by the mass inversion on the two-point correlation of the convergence maps are reduced by a factor of about 5 while the errors on the third- and fourth-order moments are reduced by a factor of about 2 and 10 respectively.

Euclid : un subtil amalgame pour un résultat cosmologique plus précis

Au terme de trois années de travail, une équipe de la collaboration Euclid, coordonnée par l’Irfu, dévoile une nouvelle méthode pour traiter conjointement les observations ciblant spécifiquement la matière noire ou l’énergie noire, deux concepts distincts mais corrélés. Résultat : une précision de l’interprétation cosmologique grandement améliorée !

La quête de l’origine de l’accélération cosmique

Déterminer la cause de l’accélération cosmique est l’un des grands défis de la cosmologie. S’agit-il d’une constante (ΛCDM) ou d’un nouveau fluide (énergie noire, DE) ? Existe-t-il une nouvelle force qui modifie la gravité telle que décrite par Einstein (gravité modifiée, MG) ?

Le CEA travaille sur l’analyse de l’énergie noire et de la gravité modifiée dans le cadre de la mission Planck de l’Agence spatiale européenne (ESA) qui a mesuré le rayonnement du fond diffus cosmologique (CMB), la lumière émise 380 000 ans après le big bang. Dans la publication finale des données, nous avons actualisé et testé différents scénarios combinant les résultats de Planck à d’autres jeux de données.

Du machine learning dans l’espace

Clefs CEA n°69 – L’intelligence Artificielle, Novembre 2019.

En astrophysique, à l’instar de nombreux autres domaines scientifiques, le machine learning est devenu incontournable ces dernières années, et pour un très large éventail de problèmes: restauration d’images, classification et caractérisation des étoiles ou des galaxies, séparation automatique des étoiles des galaxies dans les images, simulation numérique d’observations ou de distribution de matière dans l’Univers…

Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard because the frameworks used are not the same among studies, the networks are not properly re-trained, and the datasets used are not the same among comparisons. The recent release of a public dataset, fastMRI, consisting of raw k-space data, encouraged us to write a consistent benchmark of several deep neural networks for MR image reconstruction. This paper shows the results obtained for this benchmark, allowing to compare the networks, and links the open source implementation of all these networks in Keras. The main finding of this benchmark is that it is beneficial to perform more iterations between the image and the measurement spaces compared to having a deeper per-space network.

Reference:  Z. Ramzi, P. Ciuciu and J.-L. Starck. “Benchmarking MRI reconstruction neural networks on large public datasetsApplied Sciences, 10, 1816, 2020.  doi:10.3390/app10051816

Beyond self-acceleration: force- and fluid-acceleration

The notion of self acceleration has been introduced as a convenient way to theoretically distinguish cosmological models in which acceleration is due to modified gravity from those in which it is due to the properties of matter or fields. In this paper we review the concept of self acceleration as given, for example, by [1], and highlight two problems. First, that it applies only to universal couplings, and second, that it is too narrow, i.e. it excludes models in which the acceleration can be shown to be induced by a genuine modification of gravity, for instance coupled dark energy with a universal coupling, the Hu-Sawicki f(R) model or, in the context of inflation, the Starobinski model. We then propose two new, more general, concepts in its place: force-acceleration and field-acceleration, which are also applicable in presence of non universal cosmologies. We illustrate their concrete application with two examples, among the modified gravity classes which are still in agreement with current data, i.e. f(R) models and coupled dark energy.

As noted already for example in [35, 36], we further remark that at present non-universal couplings are among the (few) classes of models which survive gravitational wave detection and local constraints (see [12] for a review on models surviving with a universal coupling). This is because, by construction, baryonic interactions are standard and satisfy solar system constraints; furthermore the speed of gravitational waves in these models is  cT = 1 and therefore in agreement with gravitational wave detection. It has also been noted (see for example [37–39] and the update in [33]) that models in which a non-universal coupling between dark matter particles is considered would also solve the tension in the measurement of the Hubble parameter [40] due to the degeneracy beta - H0 first noted in Ref. [41].

Reference: L.Amendola, V.Pettorino  "Beyond self-acceleration: force- and fluid-acceleration", Physics Letters B, in press, 2020.

The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

DeepMass: The first Deep Learning reconstruction of dark matter maps from weak lensing observational data (DES SV weak lensing data)


 This is the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over 3.6 x 10^5 simulated data realisations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution.  Our DeepMass method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering with the optimal known power spectrum still gives a worse MSE than our generalised method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

Reference 1:  N. Jeffrey, F.  Lanusse, O. Lahav, J.-L. Starck,  "Learning dark matter map reconstructions from DES SV weak lensing data", Monthly Notices of the Royal Astronomical Society, in press, 2019.


Euclid joint meeting: WL + GC + CG SWG + OU-LE3




February 3 - 7, 2020


IAP - Institue d'Astrophysique de Paris, 98 bis, bd Arago, 75014 Paris


The preliminary schedule can be found here:

Slides (password-protected) are on redmine.

The meeting starts on Monday 3 February at 9:30.


Participant list

Please add your name to the following list if you intend to participate. To access IAP, external people are required to indicate their name in advance of the meeting, and might have to show identification at the IAP front desk. There is no conference fee.

Practical information

How to get to IAP.

Hotel list.

Restaurant list.


Martin Kilbinger  <>

Sandrine Codis <>