The CosmoStat laboratory is an interdisciplinary research group at CEA Saclay, near Paris/France. It is part of the astrophysics division (SAp - Service d'Astrophysique / SEDI - Service Electronique Détecteurs et Informatique) at the Institute of Research into the Fundamental Laws of the Universe (IRFU). CosmoStat is also part of AIM (Astrophysics, Instrumentation and Modelling), a mixed research unit of CEA, CNRS, and Université Paris-Diderot (Paris 7). We are part of the newly created Université Paris-Saclay.

The scientific focus of CosmoStat is computational cosmology. The motivation to bring together cosmologists and computer scientists is to develop and apply new methods from statistics, signal processing, and compressed sensing to cosmology and other fields. The main CosmoStat research areas are:

Further, members of CosmoStat are involved in teaching and education:

  • Teaching: Teach students and young researchers how to analyze astronomical data.
  • Dissemination: Take opportunity to disseminate our idea and tools in and outside the astronomical field (CEA, CNRS, University, Industry...).


 

CosmoStat News

Checkout all the latest CosmoStat news, events and publications

 

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

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)

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

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