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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Research | Projects | People

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.

Statistical Challenges in 21st Century Cosmology (COSMO21)

Date: May 23-25 2018 Venue: Valencia, Spain Website: http://cosmo21.cosmostat.org/

Date: May 20-22 2018 Venue: Valencia, Spain Website: http://ada9.cosmostat.org/

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/

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

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.

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

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

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