Welcome to CosmoStat

 

The CosmoStat laboratory is an interdisciplinary research group based at CEA Saclay, near Paris, France. The group was founded in 2010 with the aim of bringing together a team of cosmologists and computer scientists in order to find new ways to approach problems in the domains of astrophysics, cosmology and signal processing.

Since its inception, CosmoStat has produced a wealth of software and publications, and contributed to the development of several excellent researchers. Members of CosmoStat have also significantly contributed to various big international projects such as Planck, Fermi, Herschel and Euclid.

 

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CosmoStat News

Checkout all the latest CosmoStat news, events and publications

 

Dictionary Learning on Manifolds

Date: September 4-6 2017 Venue: Maison du Séminaire Website: http://dlm.cosmostat.org/ Details regarding this event can be found here.
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Dictionary Learning on Manifolds

Statistical Challenges in 21st Century Cosmology (COSMO21)

Date: May 23-25 2018 Venue: Valencia, Spain Website: http://cosmo21.cosmostat.org/
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Statistical Challenges in 21st Century Cosmology (COSMO21)

Astronomical Data Analysis IX (ADA9)

Date: May 20-22 2018 Venue: Valencia, Spain Website: http://ada9.cosmostat.org/
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Astronomical Data Analysis IX (ADA9)

EuroPython 2017

Date: July 9-16 2017 Venue: Rimini, Italy Website: https://ep2017.europython.eu/en/ Blog: http://blog.europython.eu/ Twitter: @europython Conference App: https://ep2017.europython.eu/en/events/conference-app/  
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EuroPython 2017

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
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École Euclid de cosmologie 2017

Unsupervised feature learning for galaxy SEDs with denoising autoencoders

  Authors: Frontera-Pons, J., Sureau, F., Bobin, J. and Le Floc'h E. Journal: Astronomy & Astrophysics Year: 2017 Download: ADS | arXiv Abstract W.
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Unsupervised feature learning for galaxy SEDs with denoising autoencoders

PSF field learning based on Optimal Transport Distances

  Authors: F. Ngolè Mboula, J-L. Starck Journal: arXiv Year: 2017 Download: ADS | arXiv   Abstract Context: in astronomy, observing large fractions of the sky within a reasonable amount of time
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PSF field learning based on Optimal Transport Distances

Joint Multichannel Deconvolution and Blind Source Separation

  Authors: M. Jiang, J. Bobin, J-L. Starck Journal: arXiv Year: 2017 Download: ADS | arXiv   Abstract Blind Source Separation (BSS) is a challenging matrix factorization problem that plays a central
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Joint Multichannel Deconvolution and Blind Source Separation

Space variant deconvolution of galaxy survey images

  Authors: S. Farrens, J-L. Starck, F. Ngolè Mboula Journal: A&A Year: 2017 Download: ADS | arXiv Abstract Removing the aberrations introduced by the Point Spread Function (PSF) is a fundamental
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Space variant deconvolution of galaxy survey images

 


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.