Revealing which Combinations of Molecular Lines are Sensitive to the Gas Physical Parameters of Molecular Clouds
Astrophysics Meet Data Science towards the Orion B Cloud
1 IRAM, 300 rue de la Piscine, 38406 Saint Martin d’Hères, France
2 LERMA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Universités, France
3 Laboratoire d’Astrophysique de Bordeaux, Univ. Bordeaux, CNRS, France
4 Chalmers University of Technology, Department of Space, Earth and Environment, Sweden
5 Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, 59651 Villeneuve d’Ascq, France
6 Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France
7 See https://www.iram.fr/~pety/ORION-B/team.html for the other affiliations
* e-mail: firstname.lastname@example.org
Published online: 7 September 2022
Atoms and molecules have long been thought to be versatile tracers of the cold neutral gas in the universe, from high-redshift galaxies to star forming regions and proto-planetary disks, because their internal degrees of freedom bear the signature of the physical conditions where these species reside. However, the promise that molecular emission has a strong diagnostic power of the underlying physical and chemical state is still hampered by the difficulty to combine sophisticated chemical codes with gas dynamics. It is therefore important 1) to acquire self-consistent data sets that can be used as templates for this theoretical work, and 2) to reveal the diagnostic capabilities of molecular lines accurately. The advent of sensitive wideband spectrometers in the (sub)- millimeter domain (e.g., IRAM-30m/EMIR, NOEMA, …) during the 2010s has allowed us to image a significant fraction of a Giant Molecular Cloud with enough sensitivity to detect tens of molecular lines in the 70 – 116 GHz frequency range. Machine learning techniques applied to these data start to deliver the next generation of molecular line diagnostics of mass, density, temperature, and radiation field.
© The Authors, Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).