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La matière noire de la toile cosmique révélée par l’effet de lentille gravitationnelle

La matière noire de la toile cosmique révélée par l’effet de lentille gravitationnelle

Posted on by Samuel Farrens

Une nouvelle étape a été franchie dans le domaine des lentilles gravitationnelles faibles (weak lensing) avec la production d’un des plus riches catalogues de galaxies. Ce catalogue contient la morphologie ultra-précise de 100 millions de galaxies lointaines, permettant de mesurer les déformations infimes causées par le lentillage gravitationnel qui agit sur la lumière se propageant à travers la toile cosmique de matière noire présente dans tout l’Univers.

Une gigantesque cartographie du ciel et un jeu considérable de données pour mieux comprendre la matière noire

Une gigantesque cartographie du ciel et un jeu considérable de données pour mieux comprendre la matière noire

Posted on by Samuel Farrens

Au sein de la collaboration internationale UNIONS, des scientifiques de l’Institut de recherche sur les lois fondamentales de l’Univers du CEA ont produit un des plus grands jeux de données sur la matière noire, provenant de l’observation de 100 millions de galaxies déformées par des lentilles gravitationnelles. Des données très précieuses pour de nombreuses missions scientifiques. 

ShapePipe: A modular weak-lensing processing and analysis pipeline

ShapePipe: A modular weak-lensing processing and analysis pipeline

Posted on by Samuel Farrens

 

Authors: S. Farrens, A. Guinot, M. Kilbinger, T. Liaudat , L. Baumont, X. Jimenez, A. Peel , A. Pujol , M. Schmitz, J.-L. Starck, and A. Z. Vitorelli
Journal: A&A
Year: 2022
DOI: 10.1051/0004-6361/202243970
Download: ADS | arXiv

Abstract

We present the first public release of ShapePipe, an open-source and modular weak-lensing measurement, analysis, and validation pipeline written in Python. We describe the design of the software and justify the choices made. We provide a brief description of all the modules currently available and summarise how the pipeline has been applied to real Ultraviolet Near-Infrared Optical Northern Survey data. Finally, we mention plans for future applications and development. The code and accompanying documentation are publicly available on GitHub.

Jean-Luc Starck reçoit la médaille Tycho Brahe de la Société astronomique européenne

Jean-Luc Starck reçoit la médaille Tycho Brahe de la Société astronomique européenne

Posted on by Samuel Farrens

​La médaille Tycho Brahe 2022 a été décernée à Jean-Luc Starck, directeur de recherche au CEA-Irfu, pour le développement de nouvelles méthodes astrostatistiques et d’outils logiciels libres qui ont permis d’optimiser l’exploitation scientifique de grands volumes de données provenant de télescopes européens. Ces avancées ont conduit à des découvertes majeures en astrophysique extragalactique et en cosmologie.

Prix Tycho Brahe 2022 de la société européenne d'astronomie attribué à Jean-Luc Starck

Prix Tycho Brahe 2022 de la société européenne d’astronomie attribué à Jean-Luc Starck

Posted on by Samuel Farrens

La médaille Tycho Brahe 2022 est décernée à Jean-Luc Starck (CEA Saclay, France) pour le développement de nouvelles méthodes astrostatistiques et d’outils logiciels libres qui ont permis d’optimiser l’exploitation scientifique de grands volumes de données provenant de télescopes Européens spatiaux et terrestres, conduisant à des découvertes majeures en astrophysique extragalactique et en cosmologie.

AMICO galaxy clusters in KiDS-DR3: measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis

AMICO galaxy clusters in KiDS-DR3: measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis

Posted on by Samuel Farrens

 

Authors: L. Ingoglia, G. Covone, M. Sereno, ..., S. Farrens, et al.
Journal: MNRAS
Year: 2022
DOI: 10.1093/mnras/stac046
Download: ADS | arXiv

Abstract

Galaxy clusters are biased tracers of the underlying matter density field. At very large radii beyond about 10 Mpc/\textit{h}, the shear profile shows evidence of a second-halo term. This is related to the correlated matter distribution around galaxy clusters and proportional to the so-called halo bias. We present an observational analysis of the halo bias-mass relation based on the AMICO galaxy cluster catalog, comprising around 7000 candidates detected in the third release of the KiDS survey. We split the cluster sample into 14 redshift-richness bins and derive the halo bias and the virial mass in each bin by means of a stacked weak lensing analysis. The observed halo bias-mass relation and the theoretical predictions based on the \\Lambda\CDM standard cosmological model show an agreement within \2\sigma\. The mean measurements of bias and mass over the full catalog give \M_{200c} = (4.9 \pm 0.3) \times 10^{13} M_{\odot}/\textit{h}\ and \b_h \sigma_8^2 = 1.2 \pm 0.1\. With the additional prior of a bias-mass relation from numerical simulations, we constrain the normalization of the power spectrum with a fixed matter density \\Omega_m = 0.3\, finding \\sigma_8 = 0.63 \pm 0.10\.

Deep transfer learning for blended source identification in galaxy survey data

Deep transfer learning for blended source identification in galaxy survey data

Posted on by Samuel Farrens

 

Authors: S. Farrens, A. Lacan, A. Guinot, A. Z. Vitorelli
Journal: A&A
Year: 2022
DOI: 10.1051/0004-6361/202141166
Download: ADS | arXiv

Abstract

We present BlendHunter, a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SExtractor) as a function of noise level and the separation between sources. We find that BlendHunter outperforms SEP by ~ 15% in terms of classification accuracy for close blends (< 10 pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends (>10 pixel separation between sources) provided the network is trained on data with noise that has a relatively close standard deviation to that of the target images. The code and data have been made publicly available to ensure the reproducibility of the results.

Energia oscura primordiale? Se c’era, era poca

Energia oscura primordiale? Se c’era, era poca

Posted on by Samuel Farrens

Un team di cosmologi ha ricostruito la storia dell’energia oscura attraverso la cronologia dell’universo usando una varietà di dati recenti, dalla radiazione di fondo alle supernove, dimostrando che questa componente, oggi dominante, era presente al più in quantità modeste durante le prime fasi del cosmo. Secondo questo studio, una simile ‘early dark energy’ non risolve del tutto le tensioni tra modello cosmologico standard e osservazioni. Ne parliamo con una delle autrici, Valeria Pettorino del Cea di Parigi

 

NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction

Posted on by Jean-Luc Starck

Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the potential of unrolled networks for the non-Cartesian acquisition setting. We design the NC-PDNet, the first density-compensated unrolled network and validate the need for its key components via an ablation study. Moreover, we conduct some generalizability experiments to test our network in out-of-distribution settings, for example training on knee data and validating on brain data. The results show that the NC-PDNet outperforms the baseline models visually and quantitatively in the 2D settings. Additionally, in the 3D settings, it outperforms them visually. In particular, in the 2D multi-coil acquisition scenario, the NC-PDNet provides up to a 1.2 dB improvement in peak signal-to-noise ratio (PSNR) over baseline networks, while also allowing a gain of at least 1 dB in PSNR in generalization settings. We provide the opensource implementation of our network, and in particular the Non-uniform Fourier Transform in TensorFlow, tested on 2D multi-coil and 3D data.

Reference: Z. Ramzi,  Chaithya G.R.,  J.-L. Starck and P. Ciuciu “NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction”.

This conference paper presents an adaptation of unrolled networks to the challenging setup of Non-Cartesian MRI Reconstruction. It also introduces the implementation of the Non-Uniform Fast Fourier Transform in TensorFlow: tfkbnufft.
It has been accepted at ISBI 2021.