Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
Journal article, 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
Author
Lucas Einig
Institut de Radioastronomie Millimétrique (IRAM)
Grenoble Alpes University
J. Pety
Paris Observatory
Institut de Radioastronomie Millimétrique (IRAM)
Antoine Roueff
University of Toulon
Paul Vandame
Grenoble Alpes University
Jocelyn Chanussot
Grenoble Alpes University
M. Gerin
Paris Observatory
Jan Orkisz
Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics
Pierre Palud
Paris Observatory
University of Lille
Miriam G. Santa-Maria
CSIC - Instituto de Fisica Fundamental (IFF)
Victor De Souza Magalhaes
Institut de Radioastronomie Millimétrique (IRAM)
Ivana Bešlić
Paris Observatory
Sébastien Bardeau
Institut de Radioastronomie Millimétrique (IRAM)
E. Bron
Paris Observatory
Pierre Chainais
University of 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, Space, Earth and Environment, Astronomy and Plasmaphysics
David Languignon
Paris Observatory
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
Paris Observatory
Franck Le Petit
Paris Observatory
K. I. Öberg
Harvard-Smithsonian Center for Astrophysics
Nicolas Peretto
Cardiff University
Evelyne Roueff
Paris Observatory
A. Sievers
Institut de Radioastronomie Millimétrique (IRAM)
Pierre Antoine Thouvenin
University of Lille
P., Tremblin
University Paris-Saclay
Astronomy and Astrophysics
0004-6361 (ISSN) 1432-0746 (eISSN)
Vol. 677 A158Subject Categories
Probability Theory and Statistics
Signal Processing
DOI
10.1051/0004-6361/202346064