Astrophysics meets data science

Project organisation

The Hyperstars collaboration is a combination of two experts in astrophysics specialized in hyper-spectral data and numerical simulations of the star formation process, and five experts in data science covering a large range of expertise: clustering, data mining, machine learning, data quality, inverse problem, source separation, and optimisation on GPU architecture. All participants of the project are in the Paris area.

Hyperstars is funded by Mastodons, an initiative of the Mission Interdisciplinarité du CNRS, and by DIM-ACAV+.

Team members

Laboratoire Astrophysique, Instrumentation et Modélisation (AIM), CEA Saclay

  • Marc-Antoine Miville-Deschênes : CNRS directeur de recherche and scientific coordinator of Hyperstars. Analysis of astrophysics data applied to the star formation process.
  • Patrick Hennebelle : CEA scientist. Numerical simulations of the star formation process, from Galactic scales to proto-planetary disks.
  • Antoine Marchal : Ph. D. student working on a study of thermal instability of the atomic gas using a segmentation of hyperspectral 21 cm data.
  • Gerardo Ramon-Fox : Post-doc working on numerical simulations and synthetic observations of molecular clouds formation.

Laboratoire d’Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie

  • Marie-Jeanne Lesot : Maître de conférences. machine learning, data mining, clustering

Laboratoire d’Informatique Avancée de Saint Denis (LIASD), Université Paris-8

  • Adrien Revault d’Allonnes : Maître de conférences. data quality, information quality, clustering, big data processing

Laboratoire des Signaux et Systèmes (L2S), CentraleSupelec

  • Nicolas Gac : Maître de conférences at Université Paris-Sud. parallel computing, optimisation of code to GPU architecture.
  • François Orieux : Maître de conférences at Université Paris-Sud. inverse problems in the context astronomical data.
  • Charles Soussen : Professor at CentraleSupelec. source separation applied to hyper-spectral data in astronomy.