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École Euclid de cosmologie 2017

Date: June 27 - July 8 2017

Venue: Fréjus, France

Website: http://ecole-euclid.cnrs.fr/programme-2017


Lecture ``Weak gravitational lensing'' (Le lentillage gravitationnel), 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 1+2, Data analysis.
    1.  We will work on a rather large (150 MB) weak-lensing catalogue from the public CFHTLenS web page. During the TD I will show instructions how to create and download this catalogue. For faster access, it will be available on the server during the school, and I will bring a few USB sticks.
      If you like, you can however download the catalogue on your laptop at home. Please have a look at the instructions (available soon).
    2. If you want to do the TD on your laptop, you'll need to download and install athena (the newest version 1.7).
    3.  For one of the bonus TD you'll need a new version of pallas.py (v 1.8beta). Download it here.
  • Lecture notes and exercise classes:
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CosmoClub: 06-04-2017

Date: April 6th 2017

Speaker: Julie Josse (Ecole Polytechnique)

Title: Missing data imputation using principal component methods


Abstract

Missing values are ubiquitous and can occur for plenty of reasons: machines that fail, survey participants who do not answer to all questions, etc. The problem of missing values is somehow exacerbated with the amount of available data: data are often multisources (several projects aim to build large repositories by compiling data from preexisting databases) and due to the wide heterogeneity of measurement methods and research objectives, these large databases often exhibit extraordinarily high number of missing values. Missing values are problematic since most statistical methods can not be applied directly on a incomplete data.
In this talk, I will present recent tools developed to handle, in a practicable way, missing values. Among them, we can note the treatment of heterogeneous data (both quantitative and categorical) as well as the possibility to go far beyond single estimations and suggest subtle ways of assessing uncertainties. I will discuss imputation methods based on (regularized) singular value decomposition that caught the attention of the community due to their ability to handle large matrices with large amount of missing entries. Then, I will show how to extend these methods to multiple imputation to get notions of confidence intervals to know which credit should be given to analyses obtained from an incomplete data set. Such multiple imputation methods also offer new ways to visualize the variability of the results due to missing values.

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CosmoClub: 30-03-2017

Date: March 30th 2017

Speaker: Matthieu Simeoni (EPFL)

Title: Bluebild: a Stable, Accurate and Efficient Imager for Radio Astronomy


Abstract

Radio astronomy imaging has been primarily focused on a planar approximation to a portion of the observed sphere, producing images of a fixed resolution. Historically, Fourier analysis played a pivotal role, with algorithmic modifications made to fit that paradigm. It was thought that spherical calculation was computationally impractical, and that inherent numerical instability meant only a dirty image (a very rough least-squares approximation) could be made. The computational and energy demands of instruments such as the planned Square Kilometre Array (SKA) have made a new approach imperative.
Here we present an efficient algorithm called Bluebild, that reconstructs directly on the celestial sphere, producing, for the first time, a true least-square estimate of the sky. Wide-field and flexible beamformed imaging follow naturally. It produces a continuous image description that may be stored independently of resolution, and sampled up to the fundamental telescope limit. A multi-scale sky decomposition becomes an intrinsic part of the process, and algorithmic linearity permits uncertainty assessment across the chain. The algorithm is fast, far simpler and more intuitive than previous methods. We show sky images produced are more accurate, and can be analysed in much more depth. Results with real LOFAR data will be presented.

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CosmoClub: 27-03-2017

Date: March 27th 2017

Speaker: Francois Meyer (University of Colorado, Boulder/INRIA)

Title: Detecting Structural  Changes in Dynamic Community Networks


Abstract

The study of time-varying (dynamic) networks (graphs) is of fundamental importance for computer network analytics. Several methods have been proposed to detect the effect of significant structural changes in a time series of graphs.
The main contribution of this work is a detailed analysis of a dynamic community graph. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. The goal of the work is to detect the time at which the graph dynamics switches from a normal evolution -- where balanced communities grow at the same rate -- to an abnormal behavior -- where communities start merging.
In order to circumvent the problem of identifying the communities, we use a metric to quantify structural changes as a function of time.  The detection of anomalies becomes one of testing the hypothesis that the graph is undergoing a significant structural change.
This is work in collaboration with Peter Wills.

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CosmoClub: 17-02-2017

Date: February 17th 2017

Speaker: Vivien Scottez (IAP)

Title: Clustering-based Redshift estimation


Abstract

The clustering of galaxies has emerged as a powerful way to estimate the redshift distribution of a sample. I will briefly review how we can get access to redshift information from the crosscorrelation function. Then, using the MICE2 simulation I will present how we can get individual redshifts for each galaxy as well as the corresponding accuracy.