Author: Martin Kilbinger

École Euclid 2023 Rodolphe Clédassou

Posted on by Martin Kilbinger

Date: August 15 – August 30, 2023

Venue: Ronce-les-Bains, France

Website: https://ecole-euclid.cnrs.fr/2023-accueil/

“Weak gravitational lensing”  (Le lentillage gravitationnel), cycle 2, Martin Kilbinger.

Find here links to the lecture notes and TD exercises.

  • Lecture notes (pdf).
  • TD: CFIS/UNIONS weak-lensing data analysis; cluster lensing.
    Please download:

École Euclid de cosmologie 2021

École Euclid de cosmologie 2021

Posted on by Martin Kilbinger

Date: August 16 - August 27, 2021

Venue: Anglet, France

Website: https://ecole-euclid.cnrs.fr/2021-accueil/

Lecture ``Weak gravitational lensing''  (Le lentillage gravitationnel), cycle 2, Martin Kilbinger.

Find here links to the lecture notes, TD exercises, "tables rondes" topics, and other information.

  • Resources.
    • A great and detailed introduction to (weak) gravitational lensing are the 2005 Saas Fee lecture notes by Peter Schneider. Download Part I (Introduction to lensing) and Part III (Weak lensing) from my homepage.
    • Check out Sarah Bridle's video lectures on WL from 2014.
  • TD cycle 2, Data analysis. TBD
  • Lecture notes.

Simulation-based cosmological inference of large galaxy surveys

Posted on by Martin Kilbinger

Simulation-based cosmological inference of large galaxy surveys

Position: PhD
Deadline:  15/04/2021
Contact: Martin Kilbinger, Valeria Pettorino, François Lanusse

Details about this position are provided in the following pdf file.

Please apply here on adum.fr, the central site to apply for this PhD.

This project proposed in the context of UDOPIA, the Université Paris-Saclay Doctoral Programme in Artificial Intelligence

shear bias

shear bias

Posted on by Martin Kilbinger

 

Authors:  M. Kilbinger, A. Pujol
Language: Python
Download: GitHub
Description: shear_bias is a package that contains tools and scripts for shear bias estimation for weak gravitational lensing analysis.

Installation

Download the code from the github repository.

git clone https://github.com/CosmoStat/shear_bias

A directory shear_bias is created. There, call the setup script to install the package.

cd shear_bias
python setup.py install