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

 

NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction

Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the...
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Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction. There is a lack of...
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Facebook challenge in AI: CEA researchers ranked 2nd

In the field of artificial intelligence, international competition is tough. So when researchers from CEA-Joliot and Irfu challenge start-ups and...
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Facebook challenge in AI: CEA researchers ranked 2nd

State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted...
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State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge

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

 

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.