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

 

Authors: N. JeffreyF. B. AbdallaO. LahavF. LanusseJ.-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

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.

F-CUR3D

 

Authors: A. Woiselle, J.L. Starck and M.J. Fadili
Language: Matlab
Download: Mac OSX | Windows
Description: A code for fast 3D curvelet transform and reconstruction.
Notes: Documentation: jmiv2010.pdf


Fast Curvelet Transform Version 1.0

Description F-CUR3D is a software, based on the MATLAB package, which contains routines for the Fast 3D Curvelet transform and reconstruction. The F-CUR3D documentation is available in PDF format.

See also the "3D curvelet algorithm description", and examples .

F-CUR3D is available for Windows and MAC.   Publications Papers related to the software:

Baolab

 

Authors: A. Labatie, J.L. Starck, M. Lachieze-Rey
Language: IDL
Download: BAOlab.zip
Description: An IDL code for studying BAO.
Notes: Contains additional C++ routines.


BAOlab is related to the study of Baryon Acoustic Oscillations (BAO) using the 2-point correlation function. It enables to perform different tasks, namely BAO detection and BAO parameter constraints. The main novelty of this approach is that it enables to obtain a model-dependent covariance matrix which can change the results both for BAO detection and for parameter constraints.

Software: BAOlab Version 1.0

  • BAOlab contains IDL and C++ routines.
  • Source code and more information are available here.

Publications

Papers related to the software: