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Description
Molecular hydrogen being unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-IR or on star counting. (Sub-)millimeter observations of numerous trace molecules are effective from ground based telescopes, but the relationships between the emission of one molecular line and the H_2_ column density (NH_2_) is non-linear and sensitive to excitation conditions, optical depths, abundance variations due to the underlying physico-chemistry. We aim to use multi-molecule line emission to infer NH_2_ from radio observations. We propose a data-driven approach to determine NH_2_ from radio molecular line observations. We use supervised machine learning methods (Random Forests) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of NH_2_, using a limited set of molecular lines as input, and the Herschel-based dust-derived NH_2_ as ground truth output. For conditions similar to the Orion B molecular cloud, we obtain predictions of NH_2_ within a typical factor of 1.2 from the Herschel-based estimates. An analysis of the contributions of the different lines to the predictions show that the most important lines are ^13^CO(1-0), ^12^CO(1-0), C^18^O(1-0), and HCO+(1-0). A detailed analysis distinguishing between diffuse, translucent, filamentary, and dense core conditions show that the importance of these four lines depends on the regime, and that it is recommended to add the N_2_H+(1-0) and CH_3_OH(2_0_-1_0_) lines for the prediction of NH_2_ in dense core conditions. This article opens a promising avenue to directly infer important physical parameters from the molecular line emission in the millimeter domain. The next step will be to try to infer several parameters simultaneously (e.g., NH_2_ and far-UV illumination field) to further test the method.
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