Author: Jean-Luc Starck

French-Chinese Days on Weak Lensing

French-Chinese Days on Weak Lensing

Posted on by Jean-Luc Starck

Date: October 4-5, 2018

Organizer:  Jean-Luc Starck and Martin Kilbinger

Venue:

Local information

CEA Saclay is around 23 km South of Paris. The astrophysics division (DAp) is located at the CEA site at Orme des Merisiers, which is around 1 km South of the main CEA campus. See http://www.cosmostat.org/link/how-to-get-to-sap/ for detailed information on how to arrive.

On 4 and 5 October, 2018, we are organizing the first French-Chinese weak-lensing meeting at DAp, CEA Saclay. 

Program:

All talks are taking place at DAp, Salle Kepler (Building 709)

Thursday, October 4

9:30 - 10:00h.  Café

10:00 - 10:15h.    Welcome & introductions

10:15 - 10:45h.   Hu Zhan,   Overview of CSS-OS

10:45 - 11:15h.   Martin Kilbinger, Overview of CFIS Weak Lensing

11:15 - 11:45h.  Jun Zhang, Fourier_Quad,  a shear measurement method in Fourier Space

11:45 - 14:00h.  Lunch at the Rotonde

14:00 - 14:30h. Morgan Schmitz,  PSF Modeling using a Graph Manifold

14:30 - 15:00h. Chengliang Wei, A full sky WL simulation with semi-analytic galaxy formation 

15:00 - 15:30h. Jean-Luc Starck,  WL Mass Mapping

15:30 - 16:00h. Zuhui Fan,  WL peak statistics

16:00 - 16:30h. Austin Peel,  Cosmology with Mass Maps

Friday, October 5

9:30 - 10:00h.      Café

10:00 - 10:30h.   Sam Farrens,   The CFIS pipeline

10:30 - 11:00h.  Ran Li,  Lensing studies of sub-structures

11:00 - 11:30h.  Axel  Guinot,  Preliminary CFIS results

11:30 - 12:00h.  Liping Fu, Shear measurement from VOICE deep survey

12:00 - 14:00h. Lunch at Les Algorithmes

14:00 - 14:30h. Jean-Charles Cuillandre, The Euclid mission and ground-based observations

14:30 - 15:00h.  Huanyuan Shan: KiDS WL studies (via skype)

15:00 - 15:30h.  Alexandre Bruckert, Machine learning for blended objects separation

15:30 - 16:00h.   Rebeca Araripe Furtado Cunha,  Optimal Transport and PSF Modeling

16:00 -  17:00h. Discussion

 

Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV

Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV

Posted on by Jean-Luc Starck

 

Authors: N. Jeffrey, F. B. Abdalla, O. Lahav, F. Lanusse, J.-L. Starck, et al
Journal:  
Year: 01/2018
Download: ADS| Arxiv

Abstract

Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three different mass map reconstruction methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. KS is a direct inversion method, taking no account of survey masks or noise. The Wiener filter is well motivated for Gaussian density fields in a Bayesian framework. The GLIMPSE method uses sparsity, with the aim of reconstructing non-linearities in the density field. We compare these methods with a series of tests on the public Dark Energy Survey (DES) Science Verification (SV) data and on realistic DES simulations. The Wiener filter and GLIMPSE methods offer substantial improvement on the standard smoothed KS with a range of metrics. For both the Wiener filter and GLIMPSE convergence reconstructions we present a 12% improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods' abilities to find mass peaks, we measure the difference between peak counts from simulated {\Lambda}CDM shear catalogues and catalogues with no mass fluctuations. This is a standard data vector when inferring cosmology from peak statistics. The maximum signal-to-noise value of these peak statistic data vectors was increased by a factor of 3.5 for the Wiener filter and by a factor of 9 using GLIMPSE. With simulations we measure the reconstruction of the harmonic phases, showing that the concentration of the phase residuals is improved 17% by GLIMPSE and 18% by the Wiener filter. We show that the correlation between the reconstructions from data and the foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% by GLIMPSE.

Ming Jiang PhD Defense

Ming Jiang PhD Defense

Posted on by Jean-Luc Starck

Event: Ming Jiang's Thesis Defence

Date: 10/11/2017

Venue: Salle Galilée, Bât: 713C (CEA-Saclay)

My thesis is approaching its final destination after 3 years of work! I am pleased to announce you that my defense will be held at 2 pm on November 10th in Galilée room. You are welcomed to my defense!

Multichannel Compressed Sensing and its Applications in Radioastronomy

The new generation of radio interferometer instruments, such as LOFAR and SKA, will allow us to build radio images with very high angular resolution and sensitivity. One of the major problems in interferometry imaging is that it involves an ill-posed inverse problem because only a few Fourier components (visibility points) can be acquired by a radio interferometer. Compressed Sensing (CS) theory is a paradigm to solve many underdetermined inverse problems and has shown its strength in radio astronomy.

This thesis focuses on the methodology of Multichannel Compressed Sensing data reconstruction and its application in radio astronomy. For instance, radio transients are an active research field in radio astronomy but their detection is a challenging problem because of low angular resolution and low signal-to-noise observations. To address this issue, we investigated the sparsity of temporal information of radio transients and proposed a spatial-temporal sparse reconstruction method to efficiently detect radio sources. Experiments have shown the strength of this sparse recovery method compared to the state-of-the-art methods.

A second application is concerned with multi-wavelength radio interferometry imaging in which the data are degraded differently in terms of wavelength due to the wavelength-dependent varying instrumental beam. Based on a source mixture model, a novel Deconvolution Blind Source Separation (DBSS) model is proposed. The DBSS problem is not only non-convex but also ill-conditioned due to convolution kernels. Our proposed DecGMCA method, which benefits from a sparsity prior and leverages an alternating projected least squares, is an efficient algorithm to tackle simultaneously the deconvolution and BSS problems. Experiments have shown that taking into account joint deconvolution and BSS gives much better results than applying sequential deconvolution and BSS.