Software and databases
CRAL staff are actively involved in the development of software for numerical simulation, modeling, data processing, and data reduction. This involvement ranges from contributing to existing open-source software (adding features, optimizations) to the complete development of new software. Many of these software tools are publicly available.
Several of them are available for download on the CRAL GitLab server.
Numerical simulation and modeling software
Contact: Rolf Walder
Description: Code package for computing 3D magnetic flows, 3D NLTE radiative transfer, and synthetic spectra
Language: Fortran
Contact: Johan Richard
Description: Gravitational lensing software for modeling the mass distribution of galaxies and clusters
Language: C
Contact: Guillaume Laibe
Description: A smoothed particle hydrodynamics and magnetohydrodynamics code for astrophysics
Language: Fortran
Contact: Benoit Commerçon & Joki Rosdahl
Description: Adaptive Mesh Refinement hydrodynamic code for self-gravitating astrophysical flows
Language: Fortran
Contact: Leo Michel-Dansac & Jeremy Blaizot
Description: Massively parallel Monte Carlo code for resonant line transfer in AMR simulations
Language: Fortran
Data processing software
Contact: Nicolas Bouché
Description: A modeling tool for extracting galaxy parameters and kinematics from any 3-dimensional data
Language: Python
LitPro
Contact: Isabelle Tallon-Bosc
Description: JMMC model fitting software
Language: Yorick + Java
Contact: Roland Bacon & Laure Piqueras
Description: MUSE Python Data Analysis Framework
Language: Python
Contact: Ferréol Soulez
Description: A common GUI to run optical interferometry image reconstruction software
Language: Java
Contact: Roland Bacon
Description: A software tool for blind detection of faint emitters in MUSE datacubes
Language: Python
Contact: Philippe Prugniel
Description: A package for analyzing astronomical spectroscopic data
Language: GDL/IDL
Database
CRAL staff also contribute to the creation and maintenance of scientific databases made available to the research community.