Deep learning denoising by dimension reduction: Application to the ORION-B line cubes

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with inhomogenous signal-to-noise ratio (SNR) are major challenges for consistent analysis and interpretation.Aims. We search for a denoising method of the low SNR regions of the studied data cubes that would allow to recover the low SNR emission without distorting the signals with high SNR.Methods. We perform an in-depth data analysis of the 13 CO and C 17 O (1 -- 0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30m telescope. We analyse 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 allows us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13 CO (1 -- 0) cube, we 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 of the SNR in voxels with weak signal, while preserving the spectral shape of the data in high SNR 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 a promising avenue. In addition

Physical and Chemical Conditions in the Horsehead Photodissociation Region

Molecular lines are used to trace the structure of the interstellar medium and the physical conditions of the gas in different environments, from high-z galaxies to protoplanetary disks. To fully benefit from the diagnostic power of molecular lines, the formation and destruction paths of the molecules, including the interplay between gas-phase and grain surface chemistry, must be quantitatively understood. Well-defined sets of observations of simple template sources are key to benchmark the predictions of theoretical models. With that motivation, this thesis is focused on the observation and analysis of an unbiased spectral line survey at 3, 2 and 1mm with the IRAM-30m telescope in the Horsehead nebula, with an unprecedented combination of bandwidth, high spectral resolution and sensitivity. Two positions were observed: the warm photodissociation region (PDR) and a cold condensation shielded from the UV field. Approximately 30 species, with up to 7 atoms plus their isotopologues, are detected. These data are complemented by high-angular resolution IRAM-PdB interferometric maps of specific species. The results of this thesis include the detection of CF+, a new diagnostic of the UV illuminated gas; the detection of a new species in the ISM, tentatively attributed to C3H+; a deep study of the abundance, spatial distribution and excitation conditions of H2CO, CH3OH and CH3CN, which reveals that photo-desorption of ices is an efficient mechanism to release molecules into the gas phase; and the first detection of the complex organic molecules, HCOOH, CH2CO, CH3CHO and CH3CCH in a PDR, which reveals the degree of chemical complexity reached in the UV illuminated neutral gas.Les raies moléculaires tracent la structure du milieu interstellaire ainsi que les conditions physiques du gaz dans différents environnements allant des galaxies à haut redshift aux disques protoplanétaires. Pour bénéficier des diagnostics moléculaires les voies de formation et de destruction des molécules doivent être comprises quantitativement, tout comme les couplages entre la chimie en phase gazeuse et solide. Des jeux bien compris de données concernant des sources simples sont essentiels pour tester les prédictions des modèles théoriques. Cette thèse présente l'analyse d'un relevé spectral systématique à 1, 2 et 3mm avec le télescope IRAM-30m dans la Tête de Cheval, offrant une combinaison inédite de bande passante, haute résolution spectrale et sensibilité, en direction de deux positions: la région de photodissociation (PDR) et une cœure froid à proximité. Environ 30 espèces avec un maximum de 7 atomes sont détectées sans compter les isotopologues. Ces données sont complétées par des cartes interférométriques IRAM-PdBI à haute résolution d'espèces spécifiques. Les résultats de cette thèse incluent la detection de CF+, un nouveau diagnostic de gaz exposé à l'UV lointain; la détection d'une nouvelle molécule interstellaire, que nous attribuons au petit hydrocarbure C3H+; une étude approfondie des molécules organiques H2CO, CH3OH et CH3CN, qui indique que la photodésorption des glaces est un mécanisme efficace pour relâcher ces molécules en phase gazeuse; et la première détection de molécules organiques complexes, comme HCOOH, CH2CO, CH3CHO et CH3CCH dans une PDR, qui révèle la complexité chimique dans le gaz neutre éclairé en UV lointain