GOLD : The Golden Cosmological Surveys Decade

This 10-week programme on the Golden Cosmological Surveys Decade will be held at the new Institut Pascal, in Paris Orsay, from 1st April 2020 to 5th June 2020. The Institut Pascal provides offices, seminar rooms, common areas and supports long-term scientific programmes. 
GOLD 2020 will include a summer school, three workshops (on Lensing, Galaxy Clustering, Theory and Interpretation of the Data). 
In-between, an active training programme will be run. We plan to host around 40 people for the whole programme, plus around 30 scientists during the workshops. 
Whether you are a PhD, a postdoc, a senior scientist and are interested in attending this programme, you can now apply. Deadline for applications: 1st October 2019.




The workshop on Computational Intelligence in Remote Sensing and Astrophysics (CIRSA) aims at bringing together researchers from the environmental sciences, astrophysics and computer science communities in an effort to understand the potential and pitfalls of novel computational intelligence paradigms including machine learning and large-scale data processing.



French-Chinese Days on Weak Lensing

Date: October 4-5, 2018

Organizer:  Jean-Luc Starck and Martin Kilbinger


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 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. 


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


Authors: N. JeffreyF. B. AbdallaO. LahavF. LanusseJ.-L. Starck, et al
Year: 01/2018
Download: ADS| Arxiv


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.