A Fully Bayesian Approach for Inferring Physical Properties with Credibility Intervals from Noisy Astronomical Data
Paper in proceeding, 2019

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 point-wise 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.

radioastronomy

Markov chain Monte Carlo

Approximate Bayesian computation

physical conditions

Author

Maxime Vono

IRIT Institut de Recherche Informatique de Toulouse

Emeric Bron

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Pierre Chainais

Centre national de la recherche scientifique (CNRS)

Franck Le Petit

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Sebastien Bardeau

Institut de Radioastronomie Millimétrique (IRAM)

Sebastien Bourguignon

École Centrale de Nantes

Jocelyn Chanussot

Grenoble Institute of Technology (Grenoble INP)

Mathilde Gaudel

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Maryvonne Gerin

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Javier R. Goicoechea

Spanish National Research Council (CSIC)

Pierre Gratier

University of Bordeaux

Viviana V. Guzman

UCatholica

Annie Hughes

Institut de Recherche en Astrophysique et Planétologie (IRAP)

Jouni Kainulainen

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

Other publications Research

David Languignon

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Jacques Le Bourlot

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Francois Levrier

ENS

Harvey S. Listz

National Radio Astronomy Observatory

Karin I. Oberg

CfA/Harvard

Nicolas Peretto

Cardiff University

Jerome Pety

Institut de Radioastronomie Millimétrique (IRAM)

Antoine Roueff

Institut Fresnel

Evelyne Roueff

LERMA - Laboratoire d'Etudes du Rayonnement et de la Matiere en Astrophysique et Atmospheres

Albrecht Sievers

Institut de Radioastronomie Millimétrique (IRAM)

Victor de Souza Magalhaes

Institut de Radioastronomie Millimétrique (IRAM)

Pascal Tremblin

Centre national de la recherche scientifique (CNRS)

2019 10TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING - EVOLUTION IN REMOTE SENSING (WHISPERS)

2158-6268 (ISSN)


978-1-7281-5294-3 (ISBN)

10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Amsterdam, Netherlands,

Subject Categories

Astronomy, Astrophysics and Cosmology

Probability Theory and Statistics

Signal Processing

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10/3/2023

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