Abstract
Context. Based on the finding that molecular hydrogen is unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-infrared, which requires space telescopes, or on star counting, which is limited in angular resolution by the stellar density. The (sub)millimeter observations of numerous trace molecules can be effective using ground-based telescopes, but the relationship between the emission of one molecular line and the H2 column density is non-linear and sensitive to excitation conditions, optical depths, and abundance variations due to the underlying physico- chemistry. Aims. We aim to use multi-molecule line emission to infer the H2 molecular column density from radio observations. Methods. We propose a data-driven approach to determine the H2 gas column densities from radio molecular line observations. We use supervised machine-learning methods (random forest) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of the H2 column density, using a limited set of molecular lines between 72 and 116 GHz as input, and the Herschel-based dust-derived column densities as “ground truth” output. Results. For conditions similar to those of the Orion B molecular cloud, we obtained predictions of the H2 column density within a typical factor of 1.2 from the Herschel-based column density estimates. A global analysis of the contributions of the different lines to the predictions show that the most important lines are 13CO(1–0), 12CO(1–0), C18O(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 that the N2H+(1–0) and CH3OH(20–10) lines be added for the prediction of the H2 column density in dense core conditions. Conclusions. This article opens a promising avenue for advancing direct inferencing of important physical parameters from the molecular line emission in the millimeter domain. The next step will be to attempt to infer several parameters simultaneously (e.g., the column density and far-UV illumination field) to further test the method.
Highlights
Atoms and molecules have long been thought to be versatile tracers of the cold neutral medium in the Universe, from high-redshift galaxies to star-forming regions and protoplanetary disks because their internal degrees of freedom bear a signature that reveals clues about the physical conditions of their environments
While the mean square error (MSE) is used in machine learning because it is simpler and, makes it faster to compute as it does not involve the computation of the square root, the results we present from this point on use the root mean square error
Our study shows that the knowledge of the emission of a small number of 3 mm molecular lines is sufficient to predict the dust-traced column density in regions where the visual extinction is mostly associated with molecular gas
Summary
Atoms and molecules have long been thought to be versatile tracers of the cold neutral medium in the Universe, from high-redshift galaxies to star-forming regions and protoplanetary disks because their internal degrees of freedom bear a signature that reveals clues about the physical conditions of their environments. Atoms and molecules are affected by many processes: photoionization and photodissociation by far-UV photons, excitation by collisions with neutrals and electrons, radiative pumping of excited levels by far-UV or IR photons, gas phase chemical reactions, condensation on grains, solid state reactions in the formed ice, (non)-thermal desorption, etc This chemical activity is tightly coupled with gas dynamics. Numerical models of interstellar clouds face the difficulty of combining sophisticated chemical codes (addressing the molecule formation and destruction processes) with turbulent gas dynamics This is a tremendous challenge given the non-linearity of fluid dynamics, the rigidity of chemical reactions, and the wide range of time scales involved (Valdivia et al 2017; Clark et al 2019). Important to acquire self-consistent data sets that can be used as templates for this theoretical work and, at the same time, to document the diagnostic capabilities of molecular lines accurately
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