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 alleviate these
degeneracies, the abundances derived from astrochemical models can be converted
into column densities and fed into radiative transfer models. This enforces
that the molecular gas composition used by the radiative transfer models be
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 on real and synthetic observations.
