Browsing by Author "Chanussot, Jocelyn"

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    Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
    (2023) Einig, Lucas; Pety, Jerome; Roueff, Antoine; Vandame, Paul; Chanussot, Jocelyn; Gerin, Maryvonne; Orkisz, Jan H.; Palud, Pierre; Santa-Maria, Miriam G.; Magalhaes, Victor de Souza; Beslic, Ivana; Bardeau, Sebastien; Bron, Emeric; Chainais, Pierre; Goicoechea, Javier R.; Gratier, Pierre; Guzman, Viviana V.; Hughes, Annie; Kainulainen, Jouni; Languignon, David; Lallement, Rosine; Levrier, Francois; Lis, Dariusz C.; Liszt, Harvey S.; Le Bourlot, Jacques; Le Petit, Franck; Oberg, Karin; Peretto, Nicolas; Roueff, Evelyne; Sievers, Albrecht; Thouvenin, Pierre-Antoine; Tremblin, Pascal
    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 (CO)-C-13 and (CO)-O-17 (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 (CO)-C-13 (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.
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    Gas kinematics around filamentary structures in the Orion B cloud
    (2023) Gaudel, Mathilde; Orkisz, Jan H.; Gerin, Maryvonne; Pety, Jerome; Roueff, Antoine; Marchal, Antoine; Levrier, Francois; Miville-Deschenes, Marc-Antoine; Goicoechea, Javier R.; Roueff, Evelyne; Le Petit, Franck; Magalhaes, Victor de Souza; Palud, Pierre; Santa-Maria, Miriam G.; Vono, Maxime; Bardeau, Sebastien; Bron, Emeric; Chainais, Pierre; Chanussot, Jocelyn; Gratier, Pierre; Guzman, Viviana; Hughes, Annie; Kainulainen, Jouni; Languignon, David; Le Bourlot, Jacques; Liszt, Harvey; Oberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal
    Context. Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained.Aims. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent (2 <= A(V) < 6 mag) and moderately dense gas (6 <= A(V) < 15 mag), which we used to analyze the kinematics over a field of 5 deg(2) around the filamentary structures.Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the (CO)-O-18(1-0) and (CO)-C-13(1-0) signals by taking the spatial coherence of the emission into account. We produced gas column density and mean velocity maps to estimate the relative orientation of their spatial gradients.Results. We identified three cloud velocity layers at different systemic velocities and extracted the filaments in each velocity layer. The filaments are preferentially located in regions of low centroid velocity gradients. By comparing the relative orientation between the column density and velocity gradients of each layer from the ORION-B observations and synthetic observations from 3D kinematic toy models, we distinguish two types of behavior in the dynamics around filaments: (i) radial flows perpendicular to the filament axis that can be either inflows (increasing the filament mass) or outflows and (ii) longitudinal flows along the filament axis. The former case is seen in the Orion B data, while the latter is not identified. We have also identified asymmetrical flow patterns, usually associated with filaments located at the edge of an H II region.Conclusions. This is the first observational study to highlight feedback from H II regions on filament formation and, thus, on star formation in the Orion B cloud. This simple statistical method can be used for any molecular cloud to obtain coherent information on the kinematics.