Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
Artikel i vetenskaplig tidskrift, 2023

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows for the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain a lot of information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with an inhomogenous signal-to-noise ratio (S/N) are major challenges for consistent analysis and interpretation. Aims. We searched for a denoising method of the low S/N regions of the studied data cubes that would allow the low S/N emission to be recovered without distorting the signals with a high S/N. Methods. We performed an in-depth data analysis of the 13CO and C17O (1-0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30 m telescope. We analyzed the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This has allowed us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13CO (1-0) cube, we were able to compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state-of-the-art procedure for data line cubes. Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase in the S/N in voxels with a weak signal, while preserving the spectral shape of the data in high S/N voxels. Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems to be a promising avenue. In addition, dealing with the multiplicative noise associated with the calibration uncertainty at high S/N would also be beneficial for such large data cubes.

Methods: data analysis

Techniques: imaging spectroscopy

ISM: clouds

Methods: statistical

Techniques: image processing

Radio lines: ISM

Författare

Lucas Einig

Institut de Radioastronomie Millimétrique (IRAM)

Université Grenoble Alpes

J. Pety

Observatoire de Paris

Institut de Radioastronomie Millimétrique (IRAM)

Antoine Roueff

Université de Toulon

Paul Vandame

Université Grenoble Alpes

Jocelyn Chanussot

Université Grenoble Alpes

M. Gerin

Observatoire de Paris

Jan Orkisz

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Pierre Palud

Observatoire de Paris

Université de Lille

Miriam G. Santa-Maria

CSIC - Instituto de Fisica Fundamental (IFF)

Victor De Souza Magalhaes

Institut de Radioastronomie Millimétrique (IRAM)

Ivana Bešlić

Observatoire de Paris

Sébastien Bardeau

Institut de Radioastronomie Millimétrique (IRAM)

E. Bron

Observatoire de Paris

Pierre Chainais

Université de Lille

J.R. Goicoechea

CSIC - Instituto de Fisica Fundamental (IFF)

P. Gratier

Laboratoire d'Astrophysique de Bordeaux

Viviana Guzman

Pontificia Universidad Catolica de Chile

A. Hughes

Institut de Recherche en Astrophysique et Planétologie (IRAP)

Jouni Kainulainen

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

David Languignon

Observatoire de Paris

Rosine Lallement

Observatoire de Paris-Meudon

F. Levrier

Laboratoire de Physique de l’Ecole Normale Supérieure

D. C. Lis

California Institute of Technology (Caltech)

Harvey Liszt

National Radio Astronomy Observatory

Jacques Le Bourlot

Observatoire de Paris

Franck Le Petit

Observatoire de Paris

K. I. Öberg

Harvard-Smithsonian Center for Astrophysics

Nicolas Peretto

Cardiff University

Evelyne Roueff

Observatoire de Paris

A. Sievers

Institut de Radioastronomie Millimétrique (IRAM)

Pierre Antoine Thouvenin

Université de Lille

P., Tremblin

Université Paris-Saclay

Astronomy and Astrophysics

0004-6361 (ISSN) 1432-0746 (eISSN)

Vol. 677 A158

Ämneskategorier

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.1051/0004-6361/202346064

Mer information

Senast uppdaterat

2023-10-20