https://doi.org/10.1051/epjconf/202226500023
Learning from model grids: Tracers of the ionization fraction in the ISM
1 LERMA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Universités, France
2 IRAM, 300 rue de la Piscine, 38406 Saint Martin d’Hères, France
3 Laboratoire d’Astrophysique de Bordeaux, Univ. Bordeaux, CNRS, France
4 Instituto de Astrofísica, Pontificia Universidad Católica de Chile, Chile
5 Chalmers University of Technology, Department of Space, Earth and Environment, Sweden
6 Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, 59651 Villeneuve d’Ascq, France
7 See https://www.iram.fr/ pety/ORION-B/team.html for the other affliations
* e-mail: emeric.bron@obspm.fr
Published online: 7 September 2022
The ionization fraction in neutral interstellar clouds is a key physical parameter controlling multiple physical and chemical processes, and varying by orders of magnitude from the UV irradiated surface of the cloud to its cosmic-ray dominated central regions. Traditional observational tracers of the ionization fraction, which mostly rely on deuteration ratios of molecules like HCO+, suffer from the fact that the deuterated molecules are only detected in a tiny fraction of a given Giant Molecular Cloud (GMC). In [1], we propose a machine learning-based, semi-automatic method to search in a large dataset of astrochemical model results for new tracers of the ionization fraction, and propose several new tracers relevant in different ranges of physical conditions.
© 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/).
