Incorporating astrochemistry into molecular line modelling via emulation

In studies of the interstellar medium in galaxies, radiative transfer models of molecular emission are useful for relating molecular line observations back to the physical conditions of the gas they trace. However, doing this requires solving a highly degenerate inverse problem. In order to alleviat...

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Bibliographic Details
Published in:Astronomy and astrophysics (Berlin) 2019-10, Vol.630, p.A117
Main Authors: de Mijolla, D., Viti, S., Holdship, J., Manolopoulou, I., Yates, J.
Format: Article
Language:English
Subjects:
Astrochemistry
Astronomical models
Emulators
Galaxies
galaxies: abundances
Gas composition
Interstellar chemistry
Interstellar matter
Inverse problems
ISM: molecules
Meteorological satellites
methods: statistical
Molecular gases
Neural networks
Parameter estimation
Radiative transfer
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Summary:In studies of the interstellar medium in galaxies, radiative transfer models of molecular emission are useful for relating molecular line observations back to the physical conditions of the gas they trace. However, doing this requires solving a highly degenerate inverse problem. In order to alleviate these degeneracies, the abundances derived from astrochemical models can be converted into column densities and fed into radiative transfer models. This ensures that the molecular gas composition used by the radiative transfer models is chemically realistic. However, because of the complexity and long running time of astrochemical models, it can be difficult to incorporate chemical models into the radiative transfer framework. In this paper, we introduce a statistical emulator of the UCLCHEM astrochemical model, built using neural networks. We then illustrate, through examples of parameter estimations, how such an emulator can be applied to real and synthetic observations.
ISSN:0004-6361
1432-0746
DOI:10.1051/0004-6361/201935973