Revealing which Combinations of Molecular Lines are Sensitive to the Gas Physical Parameters of Molecular Clouds - Observatoire de Paris Access content directly
Proceedings EPJ Web of Conferences Year : 2022

Revealing which Combinations of Molecular Lines are Sensitive to the Gas Physical Parameters of Molecular Clouds

Pierre Gratier
  • Function : Author
Jan Orkisz
  • Function : Author
Antoine Roueff
Lucas Einig
  • Function : Author
Miriam Santa-Maria
  • Function : Author
Victor de Souza Magalhaes
  • Function : Author
Sébastien Bardeau
  • Function : Author
Jocelyn Chanussot
  • Function : Author
Pierre Chainais
  • Function : Author
Javier Goicoechea
  • Function : Author
Viviana Guzman
  • Function : Author
Annie Hughes
  • Function : Author
Jouni Kainulainen
  • Function : Author
François Levrier
Darek Lis
  • Function : Author
Harvey Liszt
  • Function : Author
Jacques Le Bourlot
  • Function : Author
Karin Oberg
  • Function : Author
Nicolas Peretto
  • Function : Author
Albrecht Sievers
  • Function : Author
Pierre-Antoine Thouvenin
  • Function : Author
Pascal Tremblin
  • Function : Author

Abstract

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.
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obspm-03994470 , version 1 (19-02-2023)

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Jérôme Pety, Maryvonne Gerin, Emeric Bron, Pierre Gratier, Jan Orkisz, et al.. Revealing which Combinations of Molecular Lines are Sensitive to the Gas Physical Parameters of Molecular Clouds. EPJ Web of Conferences, 265, pp.00048, 2022, ⟨10.1051/epjconf/202226500048⟩. ⟨obspm-03994470⟩
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