Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS)

Pierre Palud (1, 2) , Pierre-Antoine Thouvenin (1) , Pierre Chainais (1) , Emeric E. Bron (2) , Franck Le Petit (2)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 LERMA - Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres

Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS) is a Python package that performs Bayesian inference of physical parameters from multispectral-structured cubes with a dedicated sampling algorithm. Thanks to this sampling algorithm, beetroots provides maps of credibility intervals along with estimated maps.

Keywords: Bayesian inference; Interstellar medium; Artificial inteligence

Domains: Applications [stat.AP]; Astrophysics [astro-ph]

Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric E. Bron, Franck Le Petit. Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS). 2024. ⟨hal-04461075⟩