Title :

A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data

Author(s) :

Vono, Maxime [Auteur]
Signal et Communications [IRIT-SC]
Goicoechea, Javier [Auteur]
Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]
Gratier, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Guzman, Viviana [Auteur]
Joint ALMA Observatory [JAO]
Hughes, Annie [Auteur]
Max-Planck-Institut für Astronomie [MPIA]
Institut de recherche en astrophysique et planétologie [IRAP]
Kainulainen, Jouni [Auteur]
Chalmers University of Technology [Göteborg]
Languignon, David [Auteur]
Laboratoire Univers et Théories [LUTH (UMR_8102)]
Bourlot, Jacques Le [Auteur]
Laboratoire Univers et Théories [LUTH (UMR_8102)]
Levrier, François [Auteur]
Astrophysique
Listz, Harvey [Auteur]
National Radio Astronomy Observatory [NRAO]
Öberg, Karin [Auteur]
Umeå University = Umeå Universitet
Bron, Emeric [Auteur]
Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]
Orkisz, Jan [Auteur]
Institut de RadioAstronomie Millimétrique [IRAM]
Peretto, Nicolas [Auteur]
School of Physics and Astronomy [Cardiff]
Pety, Jerome [Auteur]
Institut de RadioAstronomie Millimétrique [IRAM]
Roueff, Antoine [Auteur]
Université Paul Cézanne - Aix-Marseille 3
Roueff, Evelyne [Auteur]
Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]
Sievers, Albrecht [Auteur]
Instituto de RadioAstronomía Milimétrica [IRAM]
Magalhaes, Victor de Souza [Auteur]
Institut de Planétologie et d'Astrophysique de Grenoble [IPAG]
Tremblin, Pascal [Auteur]
Département d'Astrophysique (ex SAP) [DAP]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Petit, Franck Le [Auteur]
Bardeau, Sébastien [Auteur]
Institut de RadioAstronomie Millimétrique [IRAM]
Bourguignon, Sébastien [Auteur]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Chanussot, Jocelyn [Auteur]
GIPSA - Signal Images Physique [GIPSA-SIGMAPHY]
Gaudel, Mathilde [Auteur]
Astrophysique Interprétation Modélisation [AIM (UMR7158 / UMR_E_9005 / UM_112)]
Gerin, Maryvonne [Auteur]
Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]

Conference title :

WHISPERS 2019 - 10th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

City :

Amsterdam

Country :

Pays-Bas

Start date of the conference :

2019-09-24

Journal title :

Proceedings of 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS 2019)

Publisher :

IEEE

Publication date :

2019-12-05

English keyword(s) :

Markov chain Monte Carlo
Physical conditions
Approximate Bayesian computation
Radioastronomy

HAL domain(s) :

Physique [physics]
Informatique [cs]

English abstract : [en]

The atoms and molecules of interstellar clouds emit photons when passing from an excited state to a lower energy state. The resulting emission lines can be detected by telescopes in the different wavelength domains (radio, ...
Show more >The atoms and molecules of interstellar clouds emit photons when passing from an excited state to a lower energy state. The resulting emission lines can be detected by telescopes in the different wavelength domains (radio, infrared, visible, UV...). Through the excitation and chemical conditions they reveal, these lines provide key constraints on the local physical conditions reigning in giant molecular clouds (GMCs), which constitute the birthplace of stars in galaxies. Inferring these physical conditions from observed maps of GMCs using complex astrophysical models of these regions remains a complicated challenge due to potentially degenerate solutions and widely varying signal-to-noise ratios over the map. We propose a Bayesian framework to infer the probability distributions associated to each of these physical parameters, taking a spatial smoothness prior into account to tackle the challenge of low signal-to-noise ratio regions of the observed maps. A numerical astrophysical model of the cloud is involved in the likelihood within an approximate Bayesian computation (ABC) method. This enables to both infer pointwise estimators (e.g., minimum mean square or maximum a posteriori) and quantify the uncertainty associated to the estimation process. The benefits of the proposed approach are illustrated based on noisy synthetic observation maps.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

MIAI @ Grenoble Alpes

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL