Scientific context

The formation of stars is a fundamental aspect of the evolution of the Universe. Strangely it is still poorly understood as it results from an intricate combination of complex physical processes. How interstellar matter evolves from a diffuse and warm state to dense, clumpy and filamentary structures where stars are formed is explored by comparing massive numerical simulations and large astronomical observations. We now face challenges in finding the relevant metrics to compare them and the right tools to extract high-level information from such big data sets. Hyperstars is building an innovative research collaboration in that context, by joining experts of astrophysics and data science.

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Hyperstars is a truly inter-disciplinary project bringing together scientists working on star formation using observations and numerical simulation, and experts in various fields of data science, from machine learning to GPU implementation. Every partners of the collaboration is located in the Paris area. The team includes scientists from the Astrophysics Department of CEA-Saclay, L2S of CentraleSupelec, LIP6 of Université Pierre et Marie Curie and LIASD of Université Paris-8.

Hyperstars received funds from

  • Mastodons, an initiative of the Mission pour l'Interdisciplinarité du CNRS
  • SPU department of Université Paris-Saclay
  • ANU-CNRS collaboration initiative

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On-going projects


A new tool to segment hyper-spectral data with spatial regularization.

Code on github

Analysis of synthetic observations of turbulent molecular clouds

A study of the properties of interstellar turbulence deduced from hyper-spectral molecular data. This study combines simulations from the RAMSES code, synthetic observations done with Radmc3d and the analysis with ROHSA.

A new way of estimating dust parameters

Using spatial regularization and optimization techniques to estimate dust parameters.


A collaborative tool to explore the litterature related to multi-scale, multi-process, interstellar medium physics

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