Welcome to CosmoStat

 

The CosmoStat team (LCS) is composed of both cosmologists and computer scientists working together to develop new methods of statistics, signal processing, and apply them to cosmological data set. CosmoStat goals are:

  • Statistics & Signal Processing: Develop new methods for analyzing astronomical data, and especially in cosmology where the needs of powerful statistical methods are very important.
  • Cosmology: Analyze and interpret data.
  • Projects: Participation to important astronomical projects such as Euclid, etc.
  • Teaching: Teach students and young researchers how to analyze astronomical data.
  • Dissemination: Take opportunity to disseminate our idea, tools and products  in and outside the astronomical field (CEA, CNRS, University, Industry...).

From 2012 to 2018, the activity has been mainly driven by two international projects, PLANCK and Euclid, with a increasing involvement with time in Euclid.

 

 

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Research | Projects | People

 

CosmoStat News

Checkout all the latest CosmoStat news, events and publications

 

Du machine learning dans l’espace

Clefs CEA n°69 - L'intelligence Artificielle, Novembre 2019. En astrophysique, à l’instar de nombreux autres domaines scientifiques, le machine learning est...
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Du machine learning dans l’espace

Le développement d’outils statistiques innovants

Clefs CEA n°68 - Dernières Nouvelles du Cosmos, Avril 2019. Couplant mathématiques appliquées et astrophysique.
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Le développement d’outils statistiques innovants

Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are...
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Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

Beyond self-acceleration: force- and fluid-acceleration

The notion of self acceleration has been introduced as a convenient way to theoretically distinguish cosmological models in which acceleration...
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The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

DeepMass: The first Deep Learning reconstruction of dark matter maps from weak lensing observational data (DES SV weak lensing data)DeepMass This...
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The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

 

Website Credits

The CosmoStat website is a culmination of the efforts of the whole team with special thanks to Justin Burks, Marie Chicot, Samuel Farrens, Melis Irfan, Martin Kilbinger, François Lanusse, Valeria Pettorino and Morgan Schmitz.

CosmoStat logo by Birdhouse Branding.