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...).
  • Diversity: Host a diverse group of researchers from all around the world.

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

 

 

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Research | Projects | People

 

CosmoStat News

Checkout all the latest CosmoStat news, events and publications

 

Faster and better sparse blind source separation through mini-batch optimization

Sparse Blind Source Separation (sBSS) plays a key role in scientific domains as different as biomedical imaging, remote sensing or...
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Faster and better sparse blind source separation through mini-batch optimization

Multi-CCD Point Spread Function Modelling

Context. Galaxy imaging surveys observe a vast number of objects that are affected by the instrument's Point Spread Function (PSF)....
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Multi-CCD Point Spread Function Modelling

Probabilistic Mapping of Dark Matter by Neural Score Matching

The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through...
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Probabilistic Mapping of Dark Matter by Neural Score Matching

XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge

We present a modular cross-domain neural network the XPDNet and its application to the MRI reconstruction task. This approach consists...
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XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge

Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

Deep neural networks have proven extremely efficient at solving a wide range of inverse problems, but most often the uncertainty...
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Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

 

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.